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APPLICATIONS OF REMOTE SENSING
Unit Structure :
After going through this chapter you will be able to understand the
following features
1.1 Land Use/Land Cover and Wetland Mapping
1.2 Agriculture and Soil Mapping Applications
1.3 Water Resources Appli cations
1.4 Urban Planning Applications
1.1 LAND USE/ LAND COVER AND WETLAND
MAPPING

In Remote Sensing Land use/Land cover and Wetland Mapping is the
study of landscapes and the associated spatial patterns. Mainly Land
Use/Land Cover mapping also known as LU/LC are divided into classes.
The classes may refer to natural or man -made landscapes such as forest,
river, agricultural area, fallow land, urban areas etc.
Often land cover and land use are interchangeable words. However, if we
define these terms,
1) Land Use - “Land use refers to the purpose the land serves, for
example, recreation, wildlife habitat, or agriculture.” (Government of
Canada, n.d.) Mostly, land use comprises of man -made activities,
wherein planned areas such as agriculture and urban area s or places of
economic activities are planned as per land use pattern of the given
area.

2) Land Cover - Land cover on the other hand refers mostly to the
natural landscapes such as forest cover, surface water bodies such as
lakes, rivers, or land features such as mountains, plateaus and deserts.
According to NOAA, “Land cover indicates the physical land type
such as forest or open water whereas land use documents how people
are using the land.” (NOAA, 2021) Land cover is useful in forming a
baseline and id entifying change detection. In remote sensing, using
satellite data change detection analysis is carried out for both land use
and land cover.


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2 SIGNIFICANCE OF LULC MAPS
According to Geospatial Insight n.d., “Landuse and landcover maps are
significant t o map, understand and analyze physical and human
components and its impact over land during a given time -frame.
The growth of a society totally depends on its social and economical
development. This is the basic reason why socio -economic surveys are
carried out. This type of survey includes both spatial and non -spatial
datasets. LULC maps play a significant and prime role in planning,
management and monitoring programmes at local, regional and national
levels. This type of information, on one hand, provide s a better
understanding of land utilization aspects and on the other hand, it plays an
important role in the formation of policies and programme required for
development planning. For ensuring sustainable development, it is
necessary to monitor the on goi ng process on land use/land cover pattern
over a period of time. In order to achieve sustainable urban development
and to check the haphazard development of towns and cities, it is
necessary that authorities associated with the urban development generate
such planning models so that every bit of available land can be used in
most rational and optimal way. This requires the present and past land
use/land cover information of the area. LULC maps also help us to study
the changes that are happening in our ecos ystem and environment. If we
have an inch by inch information about Land Use/Land Cover of the study
unit we can make policies and launch programmes to save our
environment”. (SATPALDA : Significance of Land Use / Land Cover
(LULC) Maps, n.d.)
Using remote sensing techniques LULC maps can be prepared using two
methods:
i. Supervised Classification - In this type of classification of LULC the
user or analyst carries out a diligent or careful selection of samples
wherein different categories are chosen. For exam ple, urban areas are
chosen and given the number as class 1 then fallow land is chosen and
given the number as class 2. Similarly, all land features are classified
based on texture, location, pattern, radiometric resolution etc. and
supervised classificati on is carried out.


LU/LC map showing Change detection of same region at different time
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ii. Unsupervised classification – An unsupervised classification has
lesser level of accuracy as compared to supervised classification
because in this type of LULC classification land cover is not
supervised and hence samples are not indicated. Instead it is a software
or computer led classification wherein only based on the elements of
LULC classification automatically tak es place. Therefore, in
unsupervised classification there is no limit to the number of classes
that can be obtained. Although this is a more detailed type of
classification it may sometimes lead to low accuracy due to erroneous
sample selection or mixing o f classes.
“The goal of unsupervised classification is to automatically segregate
pixels of a remote sensing image into groups of similar spectral
character. Classification is done using one of several statistical
routines generally called “clustering” wh ere classes of pixels are
created based on their shared spectral signatures. Clusters are split and
/or merged until further clustering doesn’t improve the explanation of
the variation in the scene.” (Harbor D., Washington and Lee
University, n.d.)


(Source: Harbor D., Washington and Lee University, n.d.)
An example of unsupervised classification is given below. It has many
classes and each pixel is classified into a class or category. This gives us a
more detailed view of the land area and identifies cl asses based on many
techniques such as:
a) ISOCLASS (Using ISO Clusters)
b) Maximum Likelihood
c) Random Trees
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4 All these methods can be used in a geospatial software such as ArcGIS
and the classification can be done using a base map or satelli te imagery
that is georeferenced. Some of the images used for change detection in
remote sensing are LANDSAT, LISS -II etc.

Unsupervised classification (Source: Harbor D., Washington and Lee
University, n.d.)

3) Wetland Mapping –Wetland mapping is useful fo r mapping of
important ecosystems. It comprises of all the water resources.
“Wetlands are defined as lands that are saturated with water long
enough to cause the formation of hydric soils and the growth of
hydrophytic or water -tolerant plants. Wetlands are found in almost all
the regions of the world from the tundra to the tropics and are a critical
part of the natural environment. They have high biological diversity
and offer critical habitats for numerous flora and fauna species.
Wetlands can also provide valuable services to humans such as flood
reduction by temporarily storing and gradually releasing stormwater.
Wetlands are complex ecological systems that are formed when
hydrological, geomorphological, and biological factors work
collectively to create the necessary conditions. There are various types
of wetlands depending on the regional and local variations in soils,
topography, climate, hydrology, water chemistry, vegetation, and other
factors, including human disturbances.” (LaRocque, n.d.)
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Wetlan d Mapping (Wetland Mapping With Landsat 8 OLI, Sentinel -1,
ALOS -1 PALSAR, and LiDAR Data in Southern New Brunswick,
Canada, n.d.)

1.2 AGRICULTURE AND SOIL MAPPING
APPLICATIONS
Agriculture mapping – Remote sensing is very useful for agriculture and
soil ma pping. The index used for crop analysis in agricultural mapping is
called NDVI. It is an important index to analyze vegetation in general or
crops in particular. NDVI stands for Normalized Difference Vegetation
Index. NDVI is calculated using the following formula.
NDVI= (NIR -VIS)/(NIR+VIS)
Wherein, NDVI stands for Normalized Difference Vegetation Index
(NDVI)
NIR stands for Near Infrared (NIR)
VIS or RED stands for Visible Spectrum or Red band of the visible
spectrum.
NDVI was developed by Compton Tucker, a NASA scientist in 1977. The
index identifies healthy green vegetation cover from sickly leaves or
damaged crops. It is very useful in studying crop health, predict crop
pattern and identify areas that need attention.







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6 Application of remote sensin g in Agriculture Mapping: There are
numerous applications of remote sensing in agriculture mapping, some of
which are listed below:
a) Crop pattern – Satellite images are analyzed using Remote Sensing
techniques to identify crop pattern. Based on the refl ectivity of crops
patterns and types of vegetation are identified. Sometimes drones are also
used the agricultural field to take high -resolution photos to better analyze
the study region.
b) Forecasting - Forecasting crop production or yield can be done usi ng
remote sensing efficiently. The quantity and quality of the crops can be
determined using remote sensing techniques.
c) Assessment – Remote sensing as discussed earlier using NDVI can
very well assess crop damage. The NDVI can help in identification of
sickly crops and protect the healthy plants in time so as to assess the crop
damage accurately and minimize damage to ensure good yield.
d) Land mapping and crop estimation – Using remote sensing land
pattern can be analyzed and crop yield can be estimate d.
e) Weather forecasting – Remote sensing is alsouseful in assessing flood
damage or drought damage on crops. This is highly useful in assessing
crop growth and crop health.
SOIL MAPPING
Soil mapping is very useful, not only in agriculture but also in other areas
such as mining, quarrying activities as well as land -use planning. Using
remote sensing techniques soil mapping can be done effectively.
According to Grind GIS, following are the applications of Soil Mapping
using Remote Sensing techniques. Fol lowing are some applications of
remote sensing to analyze soil mapping,
i) Measurement of soil content -Soil has diverse contents and
components; powerful techniques are required to analyze and study this
component. Spatial data is used to determine the c ontent of the soil. In
addition to that, multispectral satellite tools are used in mapping and
recording the soil content.The spatial data collected from the satellite is
then used to make farming -related decisions. Also, the texture of the soil
can be pre dicted using multispectral data.
ii) Soils maps - Soil mapping is a vital source of information in creating
soil maps. Soil maps are used to show the distribution of the soil, among
other surveys.These maps are greatly used in soil/spatial planning,
farmin g, soil evaluation, and similar areas. Moreover, remote sensors are
airborne tools, and therefore they can easily be collected and record data
related to soil. Therefore, the technique of soil mapping is vital in this
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7 iii) Soil survey - In the proce ss of soil survey, the technique of remote
sensing is greatly used. Researchers use remote sensors to monitor
activities in soils. The spatial data from sensors is then analyzed and
recorded to update the survey documents. Also, during other soil -related
surveys, remote sensors supplement the tools used in research.
iv) Soil fertilization - Soil fertilization is directly proportional to the yield
of the land, and therefore there’s a need to study it. Images from the space
are used in soil sampling.The soil samples collected by the satellite are
analyzed, and the quality of the soil is predicted. Also, since the images
are taken from space, the crops and vegetation of a given area are used to
decide the soil’s quality and fertility.
v) Soil boundary - Soil bo undary is a term used to indicate the end of one
type of soil and the beginning of another soil. That said, powerful tools
such as remote sensors study and analyzed the soil boundaries. Remote
sensors are capable of identifying different types of soils. Du e to this
property, the soil boundary is easily identified.
vi) Soil properties - Soil is a natural component made up of many other
properties and components. Thankfully, remote sensors have eased the
process of studying properties found in soils.Remote se nsors can record
and detect the chemical components (Nitrogen, organic carbon, etc.) found
in a given soil. The remote sensors analyze the spectral reflectance, and
the collected data is used to determine the soil’s properties.
vii) Soil science - In soil science, remote sensors are greatly used to map
and survey the soil in the study of soils.” (Source: Gikunda, A. (2022,
March 31). Applications of remote sensing in soil mapping. Grind GIS -
GIS and Remote Sensing Blogs, Articles, Tutorials | easy to learn g is, love
geography. Retrieved September 13, 2022, from https:// grindgis. com/
remote -sensing/applications -of-remote -sensing -in-soil-mapping )
1.3 WATER RE SOURCES APPLICATIONS

According to India Water Portal, “Sustainable management of the
available water resource is a challenging task for the new millennium. As
stated by the World Water Council, “There is a water crisis today. But the
crisis is not having too little water to satisfy our needs. It is crisis of
managing water so badly that billions of people and the environment –
suffer badly” (World Water Council, 2000). Remote Sensing techniques
have been used effectively in integrated development and mana gement of
water resources of India (Balakrishnan, 1986). Water has very low spectral
reflectance in the visible part of the Electro Magnetic Region (EMR)
whereas snow or ice has very high spectral reflectance in visible and near
infrared (NIR) part of the EMR. Pure water absorbs nearly all incident
energy in both the near infrared and middle infrared (MIR) wavelengths.
The low reflectance of water in visible and NIR band has advantage in
Remote Sensing as water becomes clearly distinguishable from either
vegetation or soil cover throughout the reflective infrared portion.
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8 Total Radiance (R t) recorded by a Remote Sensing system over a water
body is a function of the electromagnetic energy and is given by the
equation:

Rt = R p + R s + R v + R b where, R p = Atmospheric Path Radiance

Rs = Free -surface Layer Reflectance
Rv = Subsurface Volumetric Reflectance
Rb = Bottom Reflectance

In situ Spectroradiometer measurement of clear water with various levels
of clayey and silty soil as suspended sediment shows that the reflectance
peak shifts towards longer wavelengths as more suspended sediment is
added to the water. Strong chlorophyll a absorption of blue light is
observed between wavelengths of 400 and 500 nm and strong chlorophyll
a absorption of red light is ob served at approximately 675 nm (Lillesand
and Kiefer, 2000).

Application of visual and digital Remote Sensing techniques and
integration of the remotely sensed data in specific layers through the
Geographic Information System (GIS) are used by scientists in
management of water resources and prediction of natural water related
hazards like flood and drought. Visual Remote Sensing has been
extensively used in detection of water pollution, lake eutrophication
assessment and estimation of flood damage. The tec hnique of visual image
interpretation can be used in variety of ways to help monitor water
quantity, quality and geographic distribution of water resources (Lillesand
and Kiefer, 2000). In the present paper, various methods of application of
Remote Sensing in water quality and water resources management are
discussed.” (Application of Remote Sensing in Water Quality and Water
Resources Management – an Overview, n.d.)
Some major applications of Remote Sensing in Water Resource are as
follows:
i) Assessing water quality –The impurities in water bodies is captured
by remote sensing data by analyzing the spectral reflectivity of the
water. However, as it is a complex study with some limitations the
results might vary due to the presence of various spectral signatur es,
water impurities or solar reflectivity.
ii) Runoff and Hydrological Modelling –Surface runoff and
hydrological modelling can be done using remote sensing. Patterns
such as DEM (Digital Elevation Model) Soil erosion are analyzed.
(Haralick et al., 1985). Hy drological modeling and GIS has been used
in similar studies in small watersheds in India (Hari Prasad et al.,
1997).
iii) Flood and Drought Management –Remote sensing datasets such as
IRS-1C, IRS -1D, IRS -P6, Cartosat -1, Cartosat -2, Radarsat and Earth
Resource S atellite (ERS) datasets are used and for flood inundation
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9 Similarly, to predict drought remote sensing is useful especially for
analyzing soil moisture pattern.
iv) Watershed Management –Multispectral data and baseline data is very
useful for watershed management. To analyze the sediment load, water
catchment area as well as river basin and associated patterns remote
sensing is highly useful.
v) Irrigation Command Area Management –Irrigability maps and
periodic assessment of Irrigation Command Area Management using
remote sensing techniques lead to better crop analysis and prediction
and also better irrigation patterns for agricultural lands.
1.4 URBAN PLANNING APPLI CATIONS

There are many applications of Remote Sensing in Urban Planning some
of them are listed below:

i) Land use patterns – In Urban areas a detailed analytical study of
Land use pattern is very useful. Town growth and land use patterns
can be studied using change detection technique in Remote Sensing
and town planning can be analyzed.

ii) Green zones – Remote sensing is highly useful to analyze green areas
and plan them in urban areas. Identification of fallow using remote
sensing land and conversion of land to vegetation cover can be
accomplish ed for urban areas.

iii) Residential areas – Using satellite images urban sprawl can be
studied in detail to identify if the urban sprawl can be managed or
there is need to better plan the residential areas in future.

iv) Coastal cities – As discussed previously Remote Sensing is highly
useful in prediction of flood and hence it is also useful in monitoring
and managing coastal cities or towns.

v) Disasters – Some disasters such as fire accidents, droughts or water
management can be done using Remote Sensing techniq ues especially
for urban areas if they are having high population density.

vi) Transport Planning – Remote sensing is very useful in transport
planning of urban areas. Using GIS techniques, satellite images can be
used to analyze road networks in a city and identify accident zones,
lack of connectivity and areas where there is too much congestion.
1.5 SUMMARY
Remote sensing is defined as a scientific study of studying an area or
region using satellite images that are captured remotely, that is to say from
a distance or without coming into physical contact of the study area. There
are numerous applications of Remote Sensing. Especially with the
advancement of technology and availability of resources such as satellites
capable of capturing intricate details an d high resolution data there is no
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10 1.6 CHECK YOUR PROGRESS/ EXERCISE
I. True or False
a) In Remote Sensing Landuse and Landcover are the same.
b) LU/LC maps are highly useful for a spatio -temporal study .
c) Unsuper vised classification requires minimum intervention from
researcher or remote sensing expert.
d) Supervised classification has higher detail and more number of classes.
e) Cloud cover affects the accuracy of satellite image.
II. Fill in the blanks
i) Connectivity a nd _______________ is a measure of accident zone
analysis in Transport planning:
a) Road networks
b) Area
c) Congestion
d) None of the above

ii) __________ has low spectral reflectance.
a) Water, b) Tree Cover, c) Urban dwelling, d) Cloud

iii) In Transport Planning Re mote Sensing application Accident zones can
be identified using ___________
a) Urban Sprawl b)Change detection c)Green Zones d)Connectivity

iv) __________ is a method of Unsupervised Classification?
a) ISOCLASS, b) Random Trees, c) LISS, d) LA NDSAT

v) Wetland mapping can be done using_______data:
a) LiDAR, b) Sentinel -1, c) ALOS -1 PALSAR, d) All of the above
III. Multiple Choice Questions
a. LU/LC maps are highly useful in:
1. Planning
2. Forest cover analysis
3. Transport
4. Green zones

b. Which type of remo te sensing technique is used to detect water
pollution?
1. Visual Remote Sensing
2. Change Detection
3. Supervised classification
4. Unsupervised classification


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11 c. Water has very low spectral reflectance in the visible part of which of
the following:
1. Electro Magnetic Region (EMR)
2. NIR (Near Infrared)
3. Surface Layer
4. Bottom Layer

d. Multispectral data is very useful in analyzing which of the following
applications?
1. Flood and Drought Management
2. Watershed Management
3. Land use planning
4. Agriculture planning

e. Compton Tucker, a NA SA scientist in 1977 developed which of the
following index :
1. NDVI
2. NID
3. ISOCLASS
4. Maximum Likelihood
1.10 ANSWERS TO THE SELF -LEARNING QUESTIONS
Ia. False
Ib. True
Ic. True
Id. True
Ie. True
IIa. Congestion
IIb.Cloud
IIc. Connectivity
IId. ISOCLASS
IIe.All of the above
IIIa. Planning
IIIb. Visual Remote Sensing
IIIc.Electro Magnetic Region (EMR)
IIId. Watershed Management
IIIe. NDVI
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12 1.11 TECHNICAL WORDS AND THEIR MEANING
 LULC: The term LULC refers to Land Use Land Cover mapping.
Using remote sensing lot tech niques LULC maps can be prepared.
These maps are highly useful in urban planning, forest monitoring,
agriculture planning, urban sprawl studies etc. There are two methods
used in Remote Sensing for preparing these maps.

 DEM: Digital Elevation Model is a model prepared using Remote
Sensing techniques for the study of elevation and associated land
features.

 LADSAT: LANDSAT data is highly useful in study of land features in
Remote Sensing.

 (Rt): It refers to Total Radiance which is the total radiance captu red
over a water body by electromagnetic waves.
1.12 TASK
Download 2 sets of LANDSAT data images (For 2 different years atleast 5
or 10 years apart) from the following website and attempt spatio -temporal
change detection analysis to prepare an LULC map usi ng QGIS software.
USGS: https://www.usgs.gov/centers/eros/science/usgs -eros-archive -
landsat -archives -landsat -7-enhanced -thematic -mapper -plus-etm
1.13 REFERENCES FOR FURTHER STUDY
 Land use/land cover. EO_LULC_Objective | NRSC Web Site. (n.d.).
Retrieved September 19, 2022, from https:// www. nrsc. gov. in/ EO_
LULC_Objective? language_content_entity=en

 SemiColonWeb. (2021). Significance Of Land Use / Land Cover (LULC)
Maps | SATPALDA. Satpalda.com. https:// www. satpalda. com/ blogs/
significance -of-land-use-land-cover -lulc-maps

 Unsupervised Classification – GEOL 260 – GIS & Remote Sensing. (n.d.).
Geol260.Academic.wlu.edu. https://geol260.academic.wlu.edu/course -
notes/image -classification/unsupervised -classification/

 Gikunda, A. (2022, March 31). Applications of remote sensing in soil
mapping. Grind GIS -GIS and Remote Sensing Blogs , Articles, Tutorials |
Easy to learn GIS, Love Geography. Retrieved September 13, 2022, from
https://grindgis.com/remote -sensing/applications -of-remote -sensing -in-
soil-mapping

 LaRocque, A.; Phiri, C.; Leblon, B.; Pirotti, F.; Connor, K.; Hanson, A.
Wetland Mapping with Landsat 8 OLI, Sentinel -1, ALOS -1 PALSAR, and munotes.in

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13 LiDAR Data in Southern New Brunswick, Canada. Remote Sensing.
2020, 12, 2095. https://doi.org/10.3390/rs12132095 )

 Application of Remote Sensing in Water Quality and Water Resources
Management – An Overview. (n.d.). India Water Portal Hindi. Retrieved
September 19, 2022, from https:// hindi. indiawaterportal. org/content/
application -remote -sensing -water -quality -and-water -resources -
managem ent-overview/content -type-page/53244





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2
HYPERSPECTRAL REMOTE SENSING
Unit Structure :
After going through this chapter you will be able to understand the
following features
2.1 Objectives
2.2 Introduction
2.3 Subject Discussion
2.4 Hyperspectral Imaging: Hyperspectral Concepts, data collect ion
systems, normalization, Calibration techniques
2.5 Data processing techniques and Classification techniques, Spectral
angle mapping, Spectral Mixture analysis, Spectral Matching,
Mixture tuned matched filtering
2.6 Hyper -spectral satellite systems: Se nsors, orbit characteristics,
description of satellite Systems, data processing aspects, applications
2.7 Summary
2.8 Check your Progress/Exercise
2.9 Answers to the self -learning questions
2.10 Task
2.11 References for further study
2.1 OBJECTIVES
By the end of this unit you will be able to –
 Understand the concept of Hyperspectral imaging
 Understand about the data processing techniques in Hyperspectral
imaging
 Know about Hyperspectral sensors and classification techniques
 Know about various Hyper -spectral satellite systems
2.2 INTRODUCTION
As discussed earlier remote sensing can be defined as capturing, analyzing
and representing data which is collected remotely. This data can be
collected via various platforms and techniques. Platforms can be aero plane
or satellites and data capturing equipment can be cameras or sensors.
Newest addition to techniques of remote sensing is hyperspectral
imaging.
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15 2.3 SUBJECT DISCUSSION
Remote sensing is the process of obtaining information about the earth's
surface through measurement and analysis of electromagnetic energy
reflected or emitted from terrain using devices called sensors. This
process is achieved by launching platforms for sensing, designing the path
and orbits, and collecting remotely sensed data, pro cessing the information
collected and finally interpreting the results. Newest addition
Hyperspectral Imaging is one of the advanced techniques developed in
recent years. The technique was developed for in -depth detection of
minerals, natural vegetation a nd objects in physical and man -made
environment. Earlier this technique was used by scientists in laboratories
as imaging spectroscopy for detecting mineral composition.
2.4 HYPERSPECTRAL IMAGING: HYPERSPECTRAL
CONCEPTS, DATA COLLECTION SYSTEMS,
NORMAL IZATION, CALIBRATION TECHNIQUES
2.4.1 Hyperspectral Concepts :
The process of remote Sensing involves insolation from the source of
energy - sun incase of passive remote sensing. The energy falls on the
objects of the earth surface. Depending upon the inher ent properties of the
surface objects a certain amount of energy is reflected back from the
earth’s objects. (Autade, M.A. GEOGRAPHY, IDOL)This energy is
captured through sensors installed in remote sensing equipment - satellites
in the form of EM radiatio n. Electromagnetic radiation covers a large
range of wavelengths. In remote sensing the maximum wavelength of
EMR is concerned with the rad iation from the visible range of EMR (i.e.
0.4 to 0.7 μm), to the radar wavelength region. (Autade, M.A.
GEOGRAPHY, IDOL)

Table 2.1 Principal divisions of Electromagnetic Spectrum (Autade, M.A.
GEOGRAPHY, IDOL)
Imaging sensors produces data which can be converted into visualized
data. This data is spectral in nature, which has Digital numbers( DN).
These numbers later on can be converted into spatial data. These data
capturing sensors can also be categorized based on the number of spectral
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16 These sensors can cover all the visible spectrum with gray scale.
Multispectral sensors on the other hand can capture visible, thermal as
well as microwave spectrum at a time. (Ranade, M.A. GEOGRAPHY ,
IDOL) For e.g. Landsat Satellite with Multispectral sensor.

Table 2.2 LANDSAT resolution specifications (USGS)
In the above table only four bands ( Band 4, 5, 6, and 7) are shown thus
only four bands are available for the imaging purpose. With the
adva ncement in the sensors the availability of bands becomes 7 to 8 in
Multispectral scanning.
HoweverIf the spectrum is extended with narrow intervals then it
becomes Hyperspectral sensors. This sensor focuses on minute details of
the spectrum and produces h igh definition images. Hyperspectral remote
sensing systems record 100s of spectral bands of relatively narrow
bandwidth simultaneously. The narrow bandwidths can reach upto 5 to
10nm. With such narrow detail capturing the objects on the earth surface
are easily identified. This technique is useful specially for change in
natural vegetation and minerals, which can not be done with multispectral
or panchromatic images. In Multispectral imaging several spectrum bands
are created wherein Hyperspectral imaging continuous spectrum is
collected. ( Figure 2.1) Hyperspectral remote sensing unlike multispectral,
breaks the spectral bands into several spectral parts which gives precise
results.
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17 Multispectral Hyperspectral Separate spectral bands Wide band width Coarse representation of
spectral signature Unable to detect minute
details Few calibration problems Small data volume Continuous or no spectral gaps Narrow band width Complete representation of spectral
signature Able to detect minute details Calibration is time consuming Large data volume
Table 2.1 Comparison between Multispectral and Hyperspectral Remote
sensing ( Introduction to Remote Sensing)
2.4.2 Data Collection systems
Hyperspectral imagery is typically collected (and represented) as a data
cube with spatial information collected in the X -Y plane, and spectral
information represented in the Z -direction. (www.csr.utexas.edu). (Figure
2.2)

Figure 2.2 Hyperspectral Imagery X -Y plane, and spectral information
represented in the Z -direction (Image Recreated)
Spectroscopy
Hyperspectral imaging technique is a combination of spectroscopy and
imaging. Spectroscopy means understanding and studying ab sorption and
emission of light -radiation by object. (Figure 2.3) Figure 2.1 Comparison between Multispectral and Hyperspectral Images
(Image Recreated from edmundoptics)
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18

Figure 2.3 Spectrometer collecting Data ( Image Recreated from
atascientific)
For example, Following are the specifications of Digital Airborne Imaging
Spectrometer
Name of the Band Number of Bands
Visible and Near infra -red (0.4 -1.0
microns) 27
Short wave infrared (1.0 -1.6 microns) 2
Short wave infrared important for
mapping clay minerals (2.0 -2.5 microns) 28
Thermal infrared 6
Table 2.2 Specifications of Digital Airborne Imaging Spec trometer
In the above table (Table 2.1) each spectral band is further divided into
several bands each observing the reflectance of that particular object. This
technique of spectroscopy observes each emission and reflectance of EMR
from an object, which ca n create more clear imagery.
Four parameters to describe the spectrometer’s capability :
➢ Spectral range
➢ Spectral bandwidth
➢ Spectral sampling
➢ Signal -to-noise ratio
Parameters like Spectral range and Spectral bandwidth are crucial when it
comes to detection and interpretation of data.
Range of Spectrometers is widespread:
● Ultraviolet (UV) - 0.001 to 0.4 µm
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Hyperspectral Remote Sensing
19 ● Near -infrared (NIR) - 0.7 to 3.0 µm
● Mid-infrared (MIR) - 3.0 to 30 µm (covers thermally emitted energy)
● Far infrared (FIR) - 30 µm to 1 mm
Spectral bandwidth is the width of an individual spectral channel in the
spectrometer. (gisresources) Broad bandwidth can collect limited
information whereas narrow bandwidth can give acute spectral
information.
2.4.3 Calibration and Normalization
Calibration
Size and proper geometry of data is important in spatial data. Geometric
scale and measurement of digital image can be varied and biased due to
many factors. This can lead to distortion in the final result. Therefore for
accurate and reliable re sults Calibration of geometric measurements is
required. Non - collaborated results can compromise on quality of data.
In the atmosphere solar radiation experiences scattering and absorption
along with reflection. Where hyperspectral sensors are intereste d in
reflections, scattering and absorption can create error in collected data.
Calibration method reduces this error with proper measurement and scale.
Calibration method also converts the sensor's radiance value to surface
reflectance values.
Advantages of Calibration spectra are:
● The calibrated data can be compared with the field and spectra of
known materials.
● The calibrated data can identify and relate them to chemical and
physical properties of materials.
Thus data calibration plays a very important role of rectifying,
understanding and relating data with chemical and physical properties of
materials of remotely sensed data.
Normalization
Many times data collected in hyperspectral imaging could not be corrected
with detailed radiometric correction. In such situations normalization is
another option which can be used to create error free data. Normalization
technique is used with the help of logs residuals. These log residuals
depend upon radiance as well as reflectance in data. Log residuals help to
identify topographic and atmospheric noise and produce error free data.
Hyperspectral remote sensing data is much larger than multispectral data,
thus collecting training samples is difficult. For better results in
normalization, an excessive number of sam ples needs to be taken into
consideration, as accuracy of the normalization depends upon the ratio of
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20 used to remote error from data. For example reflections and radiation
sample data of clouds can be used for interpretation in other dataset.
However the procedure is not simple and requires multiple log residuals.
2.5 DATA PROCESSING TECHNIQUES;
N-DIMENSIONAL SCATTER PLOTS, SPECIAL
ANGLE MAPPING, SPECTR AL MIXTURE
ANALYSIS, SPECTRAL MATCHING, MIXTURE
TUNED MATCHED FILTERING
2.5.1 N -dimensional scatter plots
N-dimensional scatter plots
Hyperspectral data (or spectra) can be thought of as points in an n -
dimensional scatterplot. The data for a given pixel co rresponds to a
spectral reflectance for that given pixel. The distribution of the
hyperspectral data in n -space can be used to estimate the number of
spectral endmembers and their pure spectral signatures and to help
understand the spectral characteristics of the materials which make up that
signature. (www.csr.utexas.edu)

Figure 2.4 Spectral Profile (Image recreated from photonics)
2.5 DATA PROCESSING TECHNIQUES AND
CLASSIFICATION TECHNIQUES, SPECTRAL
ANGLE MAPPING, SPECTRAL MIXTURE
ANALYSIS, SPECTRAL M ATCHING, MIXTURE
TUNED MATCHED FILTERING
2.5.1 Classification techniques
Images acquired by remote sensing techniques need classification for
better interpretation. Image classification is assigning similar spectral
signature pixels together, creating a s et. This set becomes a class, which munotes.in

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21 can be identified as a feature or an object with visual interpretation. As
discussed earlier (Figure 2.2) hyperspectral image is collected in 3 D
format. Two dimensions of image and one dimension of spectral
information . Thus classifying hyperspectral images needs special
classification techniques. (Figure 2. 5)

Figure 2.5 Hyperspectral image classification process ( Image recreated
from intechopen)
1. Unsupervised classification
In unsupervised classification machine or algorithm makes decisions on
how to club and which pixels to club together as a class. The decision
depends upon statistical methods like mean, Standard deviation of spectral
information. This method is further divided into Principal component
analysis and Independent component analysis. Principal Component
Analysis is a method which reduces dimensionality. In other words using
statistical methods such as standardization, covariance matrix large data
sets are broken into smaller data sets as they are easier to explore.
Independent component analysis successfully executes the independence
of the components with higher -order statistics, and is relatively more
suitable to encounter high dimensionality of HS images. (Gogineni and
Chaturvedi)
2. Supervised classifi cation
Using predetermined sample signatures and processing images for
separating class sets is called supervised classification. Predetermined
classes such as land use, agriculture, various resources can be used as
samples to classify data sets. Supervi sed classification can be done using
methods like maximum likelihood and nearest neighbor classifier.
The maximum likelihood classifier assumes that the statistics for each
class in each band are normally distributed and estimates the probability
that a g iven pixel belongs to a certain specific class. Nearest Neighbor
classifier operates on majority voting rule, presumes that all the neighbors
make equal contributions to the classification of the testing point.
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22 2.5.2 Spectral angl e mapping
Spectral angle mapping uses a method called Spectral Angle Mapper or
SAM. It is an automated method of classification of hyperspectral images.
It uses predetermined training classes from ASCII files and spectral
libraries. To apply SAM the data needs to be converted into reflectance
data which is equivalent to spectral libraries. This method calculates
spectral angle between image spectrum and reference spectrum. (Figure
2.6)

Figure 2.6 2D scatter plot of an image spectrum and library spectru m in a
two-band image
For every reference spectrum SAM computes spectral angle for each
image pixel. Basically it compares image spectra to individual spectra.
SAM procedure can be concluded in Figure 2.7.

Figure 2.7 SAM procedure (Image recreated from Gogineni and
Chaturvedi)
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23 2.5.3 Spectral Mixing
Data captured by an imaging spectrometer interacts with a variety of
factors. Image pixels contain a volume of total reflected energy from
materials. Spatial mixing of materials in the area represented by a s ingle
pixel results in spectrally mixed reflected signals For example, features
like shadow which may reflect a signal in pixels, are mixed with the dark
end of the spectrum for the accurate results. When a satellite captures an
image , depending on its r esolution, the pixel carries a lot of information
as it is a big patch on ground. (Figure 2.8 ) Pixels are considered mixed in
this scenario. Thus spectral mixing and unmixing becomes necessary for
proper interpretation.

Figure 2.8 A single pixel result s in spectrally mixed reflected signals
( Image Referred from Middlebury Remote Sensing )
Spectral library plays an important role in spectral mixing as mixing is
done with reference to its library contents. There are various methods of
spectral mixing modeling, like Mathematical modeling, Geometric
modeling.
In mathematical modeling the observed spectrum [a vector] is considered
to be the product of multiplying the mixing library of pure endmember
spectra [a matrix] by the end member abund ance [a vector]. The geometric
mixing model provides an alternate intuitive means to understand spectral
mixing. Mixed pixels are visualized as points in n -dimensional scatter -plot
space [spectral space], where n is the number of bands. (gisresources)
2.5.4 Spectral Matching
The availability of abundant spectral data from laboratory and field -based
spectro -radiometry and hyperspectral imagery has led to the development
of diverse spectral databases that are utilized in varied applications. This
repository o f spectral signatures are called spectral libraries. (Shanmugam
and Perumal) These spectral libraries matched with ground verification
details to verify the objects.
Spectral matching techniques are well suited for automation due to their
ability to map da ta from different sensors coupled with the reduced need
for additional data about the study area.( Figure 2.9) This process is called munotes.in

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24 spectral matching. (Parshakov 2012) This technique can be further
classified into:

Figure 2.9 Spectral Matching (Image r ecreated from gcs -
docs.s3.amazonaws)
● Similarity match
In this match spectra are unknown and they are not available in the present
spectral library.
● Identity match
In this match it is assumed that spectra are available in the present match
and need to ident ify.
● Deterministic match
In the deterministic type, algorithms are based on the geometrical and
physical aspects of the unknown and reference spectra. These include the
Euclidean Distance Measure (ED),Spectral Angle Mapper (SAM),
Spectral Correlation Meas ure (SCM), Binary Encoding(BE), and Spectral
Feature Fitting (SFF) techniques. (Shanmugam and Perumal)
● Stochastic match
These algorithms are based on the distributions of the spectral reflectance
of target pixels including Spectral InformationDivergence (S ID) and
Constrained Energy Minimization (CEM). (Shanmugam and Perumal)
2.5.5 Mixture tuned matched filtering
Matched Filtering is a type of unmixing in which only user chosen targets
are mapped. (Boardman et al., 1995) Any pixel with a value of 0 or less
would be interpreted as background . One problem can occur with
Matched Filtering; it can create false positive results. One solution to this
problem is to calculate “infeasibility”. Infeasibility is based on both noise
and image statistics. Pixels with hig h infeasibility values are considered as
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Hyperspectral Remote Sensing
25 One can use Mixture Tuned Matched Filtering (MTMF) to perform
Matched Filtering (MF) and to add an infeasibility image to the results.
Correctly mapped pixels will have an MF score above the backgr ound
distribution around zero and a low infeasibility value. ( l3harrisgeospatial)
2.6 HYPER -SPECTRAL SATELLITE SYSTEMS:
SENSORS, ORBIT CHARACTERISTICS,
DESCRIPTION OF SATELLITE SYSTEMS,
DATA PROCESSING ASPECTS, APPLICATIONS
2.6.1 Hyper -spectral satellite systems
Over the past few years new technology evolves in the field of remote
sensing. Technologies like hyperspectral remote sensing 3D, high
dimensional data with hundreds of bands are available.
Hyperspectral sensors
Hyperspectral s ensors use image spectroscopy technique to capture an
image. These spectrometers can be further divided into:
 Whiskbroom image spectrometer
Whiskbroom image spectrometer is an opto -mechanical device and can
produce about 200 continuous spectral channels. T he moving plane mirror
reflects radiation on the spectrometer resulting in a series of reflectance
array of information. For example Airborne Visible /Infrared Imaging
Spectrometer ( AVIRIS)

Figure 2.10 Working of image spectrometer (ecological
processes .springeropen)
 Pushbroom image spectrometer
This spectrometer uses a two dimensional CCD Array of detectors, which
is located at the focal plane. This separates the radiation according to
wavelength. The scanner works across tracks which determine swath. For
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26 Selected Airborne imaging spectrometer systems:
Spaceborne Hyperspectral Imagers
Sensor
(Agency) Number
of
Bands Spectral
Coverage
(nm) Band
Width
at FWHM
(nm) GIFOV
(mrad) (m) FOV
(deg)
(km) Data
Product Launch
Date
NIMS
(NASA/JPL) 504 700-5100 10 0.5 20
pixels Full
Cube flown
(extra -
terrestrial
mission)
VIMS
(NASA/JPL) 320 400-5000 15 0.5 70
pixels Full
Cube flown
(extra -
terrestrial
mission)
UVISI
(US
MILITARY) > 200 380 - 900
110-900 1 - 3 (100 - 1000) (25) Full
Cube MSX
spacecraft
(1994)
MODIS
(NASA/EOS) 36 415-2130
3750 - 4570
6720 -14240 10 - 500 (250 - 1000) (2330) Sub-
Cube EOS AM
platform
(1998)
EOS PM
platform
(2000)
MERIS
(ESA/EOS) 15
(selectabl
e) 400 - 1050 2.5 - 10
(selectable) (300) (1450) Sub-
Cube ESA-
POEM 1
AM
platform
(1998)
PRISM
(ESA/EOS) ~ 150 -
200
1
3 450-2350
3800
8000 - 12300 10 - 12
600
1000 (50) (50) Full
Cube Design
stage
CIS
(China) 30
6 VNIR
SWIR/MWIR
/TIR 20 (402) 90 Full
Cube Design
stage
HSI
(TRW) 128
256 400 - 1000
900 - 2500 5.00
6.38 (30) (7.7) Full
Cube LEO s/c
platform
(1996)
Table 2.4 Spaceborne Hyperspectral Imagers and Specifications
(Hernandez -Baquero)
Airborne Hyperspectral Imagers
Sensor
(Agency/Compa
ny)IFOV (mrad)
(GIFOV (m)) FOV(°)
(km) Data Product Period of
Operation Data Product Tentative
Launch Date
AAHIS
(SAIC) 288 433-832 6.0 Image Cube since 1994
AHS
(Daedalus) 48 440-12700 20 - 1500 Image Cube since 1994
AIS-1
(NASA/JPL)
AIS-2
(NASA/JPL) 128
128 900-2100
1200 -2400
800-1600
1200 -2400 9.3
10.6 Image Cub e
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27 AISA
(Karelsilva Oy) 286 450-900 1.56 - 9.36 Image Cube since 1993
AMSS
(GEOSCAN) 32
8
6 490-1090
2020 -2370
8500 -12000 20.0 - 71.0
60.0
550 - 590 Image Cube since 1985
ARES
(Lockheed) 75 2000 - 6300 25.0 - 70.0 Image Cub e since 1985
ASAS
(NASA/GSFC)
upgraded ASAS 29
62 455 - 873
400 - 1060 15.0
11.5 Image Cube
(7 viewing
angles)
+45(deg)/ -
45(deg)
Image Cube
up to 10
viewing angles)
+75(deg)/ -
55(deg) 1987 - 1991
since 1992
ASTER
Simulator
(DAIS 2815)
(GER) 1
3
20 700 - 1000
3000 - 5000
8000 - 12000 300.0
600 - 700
200 Image Cube since 1992
AVIRIS
(NASA/JPL) 224 400 - 2450 9.4 - 16.0 Image Cube since 1987
CASI
(Itres Research) 288
up to 15 430 - 870
(nominal) 2.9 Profiles
Image since 1989
CAMODIS
(China) 64
24
1
2 400 - 1040
2000 - 2480
3530 - 3940
10500 - 12500 10.0
20.0
410.0
1000.0 Imabe Cube since 1993
DAIS - 7915
(GER/DLR/JRC) 32
8
32
1
6 498 - 1010
1000 - 1800
70 - 2450
3000 - 5000
8700 - 12300 16.0
100.0
15.0
2000.0
600.0 Full Cube since 1994
DAIS - 16115
(GER) 76
32
32
6
12
2 400 - 1000
1000 - 1800
2000 - 2500
3000 - 5000
8000 - 12000
400 - 1000 8.0
25.0
16.0
333.0
333.0 Full Cube
Stereo since 1994
DAIS - 3715
(GER) 32
1
2
1
1 360 - 1000
1000 - 2000
2175 - 2350
3000 - 5000
8000 - 12000 20
1000
50
2000
4000 Full Cube since 1994
FLI/PMI
(MONITEQ) 288
8 430 - 805 2.5 Full Cube
(Profiles)
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28 GERIS
(GER) 24
7
32 400 - 1000
1000 - 2000
2000 - 2500 25.4
120.0
16.5 Full Cube since 1986
HSI
(SAIC) 128 400 - 900 4.3 Full Cube until 1994
HYDICE
(Navel Research
Laboratory) 206 400 - 2500 7.6 - 14.9 Full Cube since 1995
ISM
(DES/IAS/OPS) 64
64 800 - 1700
1500 - 3000 12.5
25.0 Full Cube since 1991
MAS
(Daedalus) 9
16
16
9 529 - 969
1395 - 2405
2925 - 5325
8342 - 14521 31 - 55
47 - 57
142 - 151
352 - 517 Full Cube since 1993
MAIS
(China) 32
32
7 450 - 1100
1400 - 2500
8200 - 12200 20
30
400 - 800 Full Cube 1990
MEIS
(McDonnell
Douglas) > 200 350 - 900 2.5 Full Cube since 1992
MISI
(RIT) 60
1
1
3
4 400 - 1000
1700
2200
3000 -5000
8000 -14000 10
50
50
2000
2000 Full Cube from 1996
MIVIS
(Daedalus) 20
8
64
10 433 - 833
1150 - 1550
2000 - 2500
8200 - 127000 20.0
50.0
8.0
400.0/500.0 Full Cube since 1993
MUSIC
(Lockheed) 90
90 2500 - 7000
6000 - 14500 25 - 70
60 - 1400 Full Cube since 1989
ROSIS
(MBB/GKSS/D L
R) 84
30 430 - 850 4.0/12.0 Full Cube
Sub-Cube since 1993
RTISR
(Surface Optics
Corp.) 20 or 30 400 - 700 (900) 7.0 - 14.0
(19.0) Full Cube since 1994
SFSI (CCRS) 120 1200 - 2400 10.0 Full Cube
Sub-Cube since 1994
SMIFTS
(U. of Hawaii) 75
35 1000 - 5200
3200 - 5200 (100 cm -1)
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29 TRWIS -A
TRWIS -B
TRWIS -II
TRWIS -III
(TRW) 128
90
99
396 430 - 850
430 - 850
1500 - 2500
400 - 2500 3.3
4.8
11.7
5.0/6.25 Full Cube
Full Cube
Full Cube
Full Cube since 1991
since 1991
since 1991
since 1991
Hybrid VIFIS
(U. of Dundee) 30
30 440 - 640
620 - 890 10 - 14
14 - 18 Full Cube since 1994
WIS-FDU
(Hughes SBRC) 64 400 - 1030 10.3 Full Cube 1992
WIS-VNIR
(Hughes SBRC) 17
67 400 - 600
600 - 1000 9.6 - 14.4
5.4 - 8.6 Full Cube 1995
WIS-SWIR
(Hugh es SBRC) 41
45 1000 - 1800
1950 - 2500 20.0 - 37.8
18.0 - 25.0 Full Cube 1995
Table 2.5 Airborne Hyperspectral Imagers and Specifications (Hernandez -
Baquero)
2.6.1 Applications Hyperspectral Image Analysis
1. Detecting Minerals and applying Mineral targeting and mapping
3. Detecting soil properties such as moisture, organic content, and
salinity
1. 3.To identify Vegetation species (Clark et al., 1995), study plant
canopy chemistry (Aber and Martin, 1995), and detect vegetation
stress (Merton, 1999).
4. To detect mili tary vehicles under partial vegetation canopy, and other
military targets
5. Study of atmospheric parameters like clouds, aerosol conditions and
Water vapor
6. In Oceanography: detection of phytoplankton, Investigations of water
quality, monitoring coastal eros ion.
7. Identifying Spatial distribution of snow cover, surface albedo and
snow water equivalent.
8. Oil Spills: Identifying areas affected by wind, waves, and tides, a
rapid assessment of the damage.
9. Environmentally sensitive areas can be targeted for protect ion and
cleanup. (gisresources)
2.7 SUMMARY
Remote sensing means data acquisition of EM radiation from various
platforms. We understand that Remote sensing data provides excellent
geometric, spatial, spectral, radiological and temporal information about munotes.in

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30 earth. With continuous monitoring and analysis, improved technology
provides vital information about earth and its systems. Hyperspectral
imaging is among the newest and fastest growing remote sensing
technology. In this Unit we tried to understand workings and various
platforms for Hyperspectral imaging. We also learned about various
Hyperspectral sensors.
2.8 CHECK YOUR PROGRESS/ EXERCISE
1. Answer True or False
i. Pushbroom spectrometer uses a two dimensional CCD Array of
detectors.
ii. When the spectrum is ex tended with narrow intervals then it becomes
Hyperspectral sensors.
iii. Hyperspectral imaging technique is a combination of remote and
imaging.
iv. Hyperspectral imaging is in 3D format.
v. Detection of phytoplankton is one of the applications of Hyperspectral
imag ing.
2. Answer the following Questions. (MCQ)
i. Hyperspectral remote sensing systems record 100s of spectral bands of
relatively____________bandwidth simultaneously
a. Narrow
b. Broad
c. Long
d. Bigger
ii. In________________classification machine or algorithm makes
decisions on how to club and which pixels to club together as a class.
a. supervised
b. unsupervised
c. narrow
d. suppressed
iii. _______________ is one of the automated method of classification of
hyperspectral images.
a. TMH
b. TWIIE
c. SAM
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Hyperspectral Remote Sensing
31 iv. The ______________data can be compar ed with the field and spectra
of known materials.
a. supervised
b. unsupervised
c. calibrated
d. suppressed
v. _______________ image spectrometer is an opto -mechanical device
a. Pushbroom
b. Whiskbroom
c. Forwardbroom
d. Upbroom
3. Answer the following questions.
1. Write a note on Applications of hyperspectral imaging.
2. State the difference between multispectral and hyperspectral imaging.
3. Explain spectral matching in detail.
4. Write a note on Calibration
5. Write a note on Normalization
2.9 ANSWERS TO THE SELF -LEARNING QUESTIONS
Answe r true or False
1. True
2. True
3. False
4. True
5. True
Answer the following Questions.(MCQ)
1. Narrow
2. Unsupervised
3. SAM
4. Calibrated
5. Whiskbroom
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32 2.10 TASK
Write a detailed article on various hyperspectral imaging satellite missions
in the world.
2.11 REFERENCES
1. Agrawal, N.K.(2006), Essentials of GPS (Second Edition), Book
Selection Centre, Hyderabad
2. American Society of Photogrammetry (1983): Manual of Remote
Sensing, ASP Palis
1. Church,V.A.
2. Barrett, E.G. and Curtis, L.F. (1992): Fundamentals of Remote
Sensing in Ai r Photo -interpretation, McMillan, New York. 7.
3. Bernhardsen, Tor (2002): Geographical Information Systems: An
Introduction, Third Edition, John Wiiey & Sons, Inc., New York.
4. Burrough, Peter A and McDonnell, R.A. (1998): Principles of
Geographical Informat ion Systems, Oxford University Press,
Mumbai.
5. Campbell. J. (1989): Introduction to Remote Sensing, Guilford, New
York.
6. Clarke, Keith C. (1998): Getting Started with Geographic Information
Systems, Prentice -Hall Series in Geogl. Info. Science, Prentice -Hall,
Inc. N.J.
7. Curran, Paul, J, (1988): Principles of Remote Sensing, Longman,
London.
8. Heywood, I.et al (2002): An Introduction to Geological Systems,
Pearson Education Limited, New Delhi.
9. Iliffe, J.C (2006), Datums and Map Projections for Remote Sensing,
GIS and Surveying, Whittles Publishing, New York.
10. Jonson. R. J. (2003): Remote Sensing of the Environment -An Earth
Resources
11. Perspective, Pearson Education Series in Geographical Information
Science, Keith C. Clarke (Series editor) Pearson Educators Pri vate
Limited. (Singapore), New Delhi.
12. Joseph, G. (2009): Fundamentals of Remote Sensing, Universities
Press (India) Pvt. Ltd., Hyderabad.
13. Lillesand and Thomand and Relph Kiffer (1994). Remote Sensing
and Image Interpretations, John Wiley and Sons, Inc., New York.
14. Parker, R, N. (2008),GIS and Spatial Analysis for the Social Sciences,
Routledge, New York.
15. Paul Longley (2005), Geographic Information Systems and Science,
John Wiley & Sons. munotes.in

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33 16. Pickles, John (2006), The Social Implications of geographic
Inform ation Systems, Rawat Publications, Jaipur.
17. Rafael c (2002) , Digital Image Processing , Pearson Education P.
Ltd, Singapore
18. Star, Jeffrey and John Estes (1996), Geographical Information
Systems: An Introduction, Prentice -Hall, inc., N.J.
19. Shekar, S and C hawla, S, (2009), Spatial Databases: A Tour, Pearson
Education, Delhi.
20. Tempfli, T. K., Kerle, N., Huurememan, G.C., and Janssen, L.L.F
(2009), Principles of Remote Sensing, ITC, Netherlands.
References for further reading:
1. Birkin, Mark et al (1996). Inte lligent GIS GeoInformation
International, Cambridge.
2. Chrisman, Nicholas (1997), Exploring Geographic Information
Systems, John Wiley and Sons Inc, New York
3. Curran, Paul.J.,2001,Imaging spectrometry for ecological
application,JAG,Vol.3 -Issue 4,305 -312 Ph otogrammetry and Remote
Sensing, Maryland, U.S.A.
4. Dyer, Johen.R.,1994:Application of absorption Spectroscopy of
Organic Compounds, Prentice Hall of India.
5. Hard, R.M. (1989): Digital Image Processing of Remotely Sensed
data, Academic Press, New York.
6. Lo, C .P (1986): Applied Remote Sensing, Longman, Scientific and
Technical, Harlow, Essex.
7. Lunder, D. (1959): Aerial Photography Interpretation: Principles and
Applications, McGrawHill, New York.
8. McCoy, Roger M. (2006), Field methods in Remote Sensing, Rawat
Publications, Jaipur. Prater, W.K. (1978): Digital image Processing,
John Wiley, New York.
9. Rao, D.P. (eds.)(1988): Remote Sensing for Earth Resources,
Association of Exploration Geologist, Hyderabad.
10. Rechards, John.R, and Jia, X., 1999:Remote Sensing Digi tal Image
Analysis, Springer
11. Sabins, F. (1982): Remote Sensing: Principles and Applications,
Freeman and Co., New York.
12. Schowengerdt, Robert.A, 1997:Remote Sensing Modals and Methods
for Image Processing, Academic Press.
13. Spencer, John (2003) Global Positi oning System: A Field Guide for
the Social Scientists, Blackwell Publishing, Malden, USA.
14. Tong,Q.,Tian,Q.,Pu,O., and zhao,C.,2001,Spectrscopic determination
of wheat Water status using 1650 -1850 nm spectral absorption
features, Int.J.Rs, Vol 22,No.12, 232 9-2338
15. Verrtappen, H., Th. (1977): Remote Sensing in Geomorphology,
Elsevier Scientific Publication Company, Amsterdam.
16. Warrin, R. Philipson (1997): Manual of Photographic Interpretations,
American Society for munotes.in

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34 Internet references :
1. http://speclab.cr.usg s.gov/spectral -lib.html for spectral library
2. https://builtin.com/data -science/step -step-explanation -principal -
component -analysis
3. https://crisp.nus.edu.sg/~research/tutorial/process.htm
4. https://ecampusontario.pressbooks.pub/remotesensing/chapter/chapter
-2-radiometric -measurements/
5. https://egyankosh.ac.in/bitstream/123456789/39533/1/Unit -5.pdf
6. https://gcsdocs.s3.amazonaws.com/EVWHS/Miscellaneous/Whitepap
ers/White_MineralSpace.htm
7. https://gisresources.com/fundamemtals -of-hyperspectral -remote -
sensing_2/
8. https://semiautomaticclassificationmanualv5.readthedocs.io/en/latest/r
emote_sensing.html#id31
9. https://spacejournal.ohio.edu/pdf/shippert.pdf
10. https://www.academia.edu/10024117/Spectral_matching_approaches_
in_hyperspectral_image_processing_PLEASE_SCROLL_ DOWN_F
OR_ARTICLEpage_terms_and_conditions_REVIEW_ARTICLE_Sp
ectral_matching_approaches_in_hyperspectral_image_processing
11. https://www.atascientific.com.au/spectrometry/
12. https://www.cis.rit.edu/class/simg707/Web_Pages/Survey_report.htm
13. https://www.egyankosh. ac.in/bitstream/123456789/39539/1/Unit -
10.pdf
14. https://www.intechopen.com/chapters/70188
15. https://www.l3harrisgeospatial.com/docs/mtmf.html
16. https://www.l3harrisgeospatial.com/docs/wholepixel_hyperspectral_a
nalysis_tutorial.html
17. https://www.n2yo.com/sate llites/?c=10
18. https://www.nrcan.gc.ca/maps -tools -and-publications/satellite -
imagery -and-air-photos/tutorial -fundamentals -remote -
sensing/satellites -and-sensors/radiometric -resolution/9379
19. https://www.photonics.com/Articles/Hyperspectral_Imaging_Spectros
copy_A_Look_at/a25139
20. https://www.rssj.or.jp/eng/ and Japan Association of Remote
Sensing
21. https://www2.geog.soton.ac.uk/users/trevesr/obs/rseo/types_of_sensor
.html


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35 3
AERIAL PHOTOGRAPHY
Unit Structure:
3.1 Introduction to aerial camera, factors affecting image quality,
3.2 Types of aerial photographs Photographic resolution and radiometric
Characteristics.
3.3 Fundamentals of photogrammetry: Introduction and defini tion
Simple geometry
3.4 Vertical aerial photograph Relief and tilt displacement Stereoscopy,
parallax Equation; flight planning Scale and height determination.
3.1 INTRODUCTION TO AERIAL CAMERA, FACTORS
AFFECTING IMAGE QUALITY

3.1.1 Introduction
Photographing from air is basically known as aerial photography. The
word ‘aerial’ derived in early 17th century from Latin word aerial, and
Greek word aerios . The term "photography" is derived from two Greek
words phosmeaning "light" and graphien meaning "wr iting" means
"writing by light".
Aerial photography comes under the branch of Remote Sensing.
Platforms from which remote sensing observations are made are aircraft
and satellites as they are the most wide spread and common platforms.
Aerial photography is a part of remote sensing and has wide applications
in topographical mapping, engineering, environmental science studies
and exploration for oil and minerals etc. In the early stages of
development, aerial photographs were obtained from balloons and kites
but after the invention of aircrafts in 1903 aircrafts are being used widely
for aerial photographs.
The sun provides the source of energy (electro magnetic radiation or
EMR) and the photo sensitive film acts as a sensor to record the images.
Diversificati ons observed in the images of photographs shows the
different amount of energy being reflected from the objects as recorded
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36 values of reflected electromagnetic radiation is recorded in digit al
numbers.
An aerial photograph is any photograph taken from an airborne vehicle
(aircraft, drones, balloons, satellites, and so forth). The aerial photograph
has many use sinmilitary operations; however, for the purpose of this
manual, it will be conside red primarily as a map supplement or map
substitute.
factors affecting image quality
3.1.2 Factors that influence Aerial Photography Scale
Scale is defined as the ratio of distances between two images on an aerial
photograph and the actual distance betwe en the same two points/ objects
on the ground, in other words the ratio of/H (where fis the focall ength of
the cameralen sand His the flying height above the mean terrain), shown
in figure 1. Change in scale from photograph to another is because of the
variations in flying height other factors that further affect the scale
variations are tilt and relief displacements. Aerial photograph, the image
should be of the highest quality. To guarantee good image quality, recent
distortion -free cameras are used. Som e latest versions of cameras have
image motion compensation devices to eliminate or reduce the effects of
forward motion. Depending upon there quirements, differentlens/
focallength/ film/ filtercombinations can be taken inuse.

Scale of photograph
Camera /Film/Filter
3.1.3 Combinations Aerial Cam eras
Aerial Cameras are special cameras that are built for mapping which
have high geometric and radiometric accuracy. Airborne camera are built
with exactness and purposely designed to expose a large number of
films/photographs in speedy succession with the ultimate in geometric
fidelity and quality. Aerial cameras generally have a medium to large
format, with good quality lens, a large film magazine, a mount to hold
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Aerial Photography
37 There are various types of aerial cameras such as Aerial mapping camera
(single lens), Reconnaissance camera, Strip camera, Panoramic camera,
Multi -lenscamera, multi bandaerial cameras, Digital camera.
Aerial Films:
Aerial film is multi laye r emulsion laid on a stable anti -halation base.
Generally aerial films are available in rolls that has crosss ection of about
10 in chin wide and 200 to 500 ft in length.
Types of Film:
Depending upon the suitability for different purpose and unique
situat ions variety of films are available that are used. Panchromatic and
natural color films are the two most commonly utilized films. These two
films along with infrared and false colour form the basic media used in
aerial photography. As shown below in fig.


Fig: . Types of film photographs
Panchromatic:
Panchromatic, more often termed black and white, is the most commonly
encountered film employed for photogrammetry. The sensitive layer
consists of silver salt (bromide, chloride, and halide) crystals suspen ded
in a pure gelatine coating which sits atop a plastic base sheet. The
emulsion is sensitive to the visible (0.4 - to 0.7 -µm) portion of the
electromagnetic spectrum.
Colour:
Natural colour also known as true colour film.. The multi layer emulsion
is sens itive to visible region of electro magnetic spectrum. There are
three layers of gelatine containing sensitized dyes, one each for blue
(0.4–0.5 µm), green (0.5 –0.6 µm), and red (0.6 –0.7 µm) light. Green and
red layers are also sensitive to blue wave length s. Visible light waves
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38 filter layer which halts further passage of the blue rays. Green and red
waves pass through this barrier and sensitize their respective dyes,
causing achemical reaction and thus completing the exposure and
creating a true colour image.
Infrared:
Current aerial infrared film is offered as two types: black and white in
frared and colour in frared. Black and White Infrared have the emulsion
sensitive to green (0.54 –0.6 µm), red (0.6 –0.7 µm), and part of the near
infrared (0.7 –1.0 µm) portions of the spectrum and renders a gray - scale
image.(Fig.)

Fig No: . Visible Spectrum
Colour Infrared:
Colour Infrared film is commonly termed as false colour. The multilayer
emulsion is sensitive to green (0.5–0.6µm), red (0.6–0.7µm), and part of
the near infrared (0.7–1.0µm) portions of the spectrum. A false colour
image contains red/pink hues in vegetative areas, with the colour
depending upon the degree to which the photosynt hetic process is active
(Fig:).
Fig No: . Vegetative areas
Flight Direction:
It is advisable that aerial photography is flown in tiles to cover the
chosen area in designated flight line (showninfig). Foreasiness in
handling, it is prudent to keep the numbe r of tiles to minimum. The
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Aerial Photography
39 flight direction of the strips/ tiles is there fore kept along the length of
the area.
This direction may be any suitable direction along a natural or man -
made feature and should be clearly specified. The further transmission
process and data collection is shown in fig .

Fig No: . Flight Line


Fig No: . Flight direction and signal receiving process
Time:
The time at which aerial photograph taken is very important, as long, deep
shadows tend to doubtful details, whereas undersi zed/small shadows
tendtomark out some details effectively and are generally fruitful in
improving the interpretational values of a photograph. Based on
experience, aerial photography should be flown when the sun's elevation is
30 degrees above the horizon or three hours before and after the local
noontime.
Season:
Factors such as seasonal variations in light reflectance, seasonal changes in
the vegetation cover and seasonal changes in climatological factors are the
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40 aerial photography is flown also dictates the season. For example, for
photogrammetric mapping, geological or soil survey purposes, the ground
should be as clearly visible as possible.
3.1.3 Atmospheric Conditions
As mentione d before, the presence of particles (smoke or dust) and
molecules of gases in the atmosphere tends to reduce contrast because of
scattering, especially by the heavier particles; therefore, the best time for
photography is when the sky is clear, which norma lly in India is from
November to February. The presence of dust and smoke during the
premonsoon summer months and of clouds during the monsoon months
forbids aerial photography during theseperiods.
Stereoscopic Coverage:
To examine the Earth's surface in three dimensions, aerial photography is
normally flown with a 60 % forward over lap and a 25 % side lap, to
provide full coverage of the area (Fig.7a and b).This is an essential
requirement from the photogram metric mapping point of view to obtain
data both on plan imetry and height susing the stereo scopic principle of
observation in 3-Dand measurement techniques with stereo plotting
instruments. Stereoscopic viewing also helps in interpretation, as the
model is viewed in threed imensions.

Fig (a) Overlap required to get the full coverage of area

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41 3.2.1 Types of aerial photographs Photographic resolution and
zradiometric Characteristics
Types of aerial photographs
The aerial photographs can be divid ed into:
1) On the basis of the direction / position of the axis of the camera
2) On the basis of the angles of coverage and focal length
3) On the basis of the films used in the cameras.
On the basis of the direction / position of the axis of the camera :
1) Vertica l
2) Horizontal
3) Oblique
4) Convergent
5) Trimetrogon
Vertical photograph
The axis of the camera is vertically adjusted to take the photographs. The
area covered through vertical air photos are often square in shape at the
uniform plane. I In simple words. These photographs are taken with an air
borne camera aimed vertically downward from the plane.

Horizontal Photographs
The horizontal air photos are also known as Terrestrial air photos. In the
production of such air photos, the axis of the camera is horizont al.
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42

Oblique Photographs
In the oblique air photos, the adjustment of the axis of the camera ranges
from the vertical to angular position. The areas covered by oblique air
photos assumed the shape of a trapezium. An oblique photograph is
divided into tw o types:
1. Low Oblique Photographs
2. High Oblique Photographs
Low Oblique Photographs
One which does not have the horizon showing is called a Low Oblique
Photographs and the axis of the camera is 0

High Oblique Photographs
An oblique photograph showin g the horizon is called a High Oblique
photographs and the axis of the camera is tilted to 30 to 60

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43 Convergent Photographs
The convergent air photos are also oblique, but an area is simultaneously
Photographed by two cameras.

Trimetrogen photographs
In Trimetrogen air photos, three cameras are used simultaneously amongst
which the central camera is vertical, and the other two are adjusted to
oblique position. The cameras are so fixed that the entire area from right
horizon to the left horizon is photo graphed.

On the basis of the angles of coverage and focal length
The lenses used in the camera, are of the following types according to the
angles of coverage and the focal length:
1. Narrow Angle < 60 - More Focal Length
2. Normal Angle 60 - 75
3. Wide Angle 75 - 100
4. Super wide Angle > 100 - Low Focal Length.
On the basis of the films used in the camer as.

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44 On the basis of these category the air photos are divided into three types
such as :
1. Black and white.
2. Infrared (IR)
3. Colored
Black and white P hotographs
This is also known as PANCHROMATIC. This is most widely used type
of aerial photograph. This is mainly used for study of geological mapping.
glacial deposit, coastal formation, relief features etc. this films are
cheapest and easily available.

Colored Photographs
Colored photography is mainly used for interpretation purpose. There are
three colors Yellow Magenta (Blue + Red) and Blue Green when these
three colors mixed together colors . This types of photographs for mapping
cultivated land en vironment / vegetation cover, geologs, geomorphology
etc.

3.2.2 Arial Photographic resolution
Method 1: By Establishing Relationship Between Photo Distance and
Ground Distance :
If additional information like ground distances of two identifiable points in
an aerial photograph is available, it is fairly simple to work out the scale of
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Aerial Photography
45 Provided that the corresponding ground distances (Dg) are known for
which the distances on an aerial photograph (Dp) are measured. In such
cases, the scale of an aerial photograph will be measured as a ratio of the
two, i.e. Dp/ Dg.
The distance between two points on an aerial photograph is measured as 2
centimetres. The known distance between the same two points on the
ground is 1 km. Compute the scale of the aerial photograph (Sp).
Solution
Sp = Dp: Dg
= 2 cm: 1 km
= 2cm: 1 × 100,000 cm
= 1: 100,000/2 = 50,000 cm
= 1 unit represents 50,000 units
Therefore, Sp = 1: 50,000
Method 2 By Establishing Relationship Between Photo Distance and
Map Distance:
As we know, the distances between different points on the ground are not
always known. However, if a reliable map is available for the area shown
on an aerial photograph, it can be used to determine the photo scale. In
other words, the distance s between two points identifiable both on a map
and the aerial photograph enable us to compute the scale of the aerial
photograph (Sp). The relationship between the two distances may be
expressed as under: (Photo scale: Map scale) = (Photo distance: Map
distance)
We can derive
Photo scale (Sp) = Photo distance (Dp): Map distance (Dm) × Map
scale factor (msf)
The distance measured between two points on a map is 2 cm. The
corresponding distance on an aerial photograph is 10 cm. Calculate the
scale of the pho tograph when the scale of the map is 1:50,000.
Solution
Sp = Dp: Dm × msf
Or = 10 cm: 2 cm × 50,000
Or = 10 cm: 100,000 cm
Or = 1: 100,000/10 = 10,000 cm
Or = 1 unit represents 10,000 units
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46 Method 3 By Establishing Relationship Be tween Focal Length (f) and
Flying Height (H) of the Aircraft:
If no additional information is available about the relative distances on
photograph and ground/map, we can determine the photo -scale provided
the information about the focal length of the camer a (f) and the flying
height of the aircraft (H) are known (Fig. 6.15). The photo scale so
determined could be more

Figure ………. Focal Length of the Camera (f) and Flying Height of the
Aircraft (H)
reliable if the given aerial photograph is truly vertical or near vertical and
the terrain photographed is flat. The focal length of the camera (f) and the
flying height of the aircraft (H) are provided as marginal information on
most of the vertical photographs (Box 6.2).
The Fig. 6.15 may be used to derive the photo -scale formula in the
following way :
Focal Length (f): Flying Height (H) =
Photo distance (Dp): Ground distance (Dg)
Compute the scale of an aerial photograph when the flying height of the
aircraft is 7500m and the focal length of the camera is 15cm.
Sp = f: H
Or Sp = 15 cm : 7,500 × 100 cm
Or Sp = 1: 750,000/15
Therefore, Sp = 1: 50,000

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47

793 is a Photo Specification number maintained by the 73 APFPS Party of
the Survey of India. B is the Flying Agency that carried out the present
photography (In India three flying agencies are officially permitted to
carry out aerial photography. They are the Indian Air Force, the Air
Survey Company, Kolkata and the National Remote Sensing Agency,
Hydrabad, identified on the aerial photographs as A, B and C
respec tively), 5 is the strip number and 23 is the photo number in strip 5.
3.3.1 Fundamentals of photogrammetry: Introduction and definition
Simple geometry
Fundamentals of photogrammetry
PHOTOGRAMMETRY IS…
A means of obtaining information from aerial photogra phs
PHOTOGRAMMETRY IS THE SCIENCE

Photo = “Picture“,
► Grammetry = “Measurement“, therefore
► Photogrammetry = “photo -measurement”

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48 Definition of Photogrammetry:
The art, science, and technology of obtaining information about physical
objects and the environment by photographic and electromagnetic images ,
in order to determine characteristics such as size, shape and position of
photographed objects.
WHAT IS PHOTOGRAMMETRY
 Photogrammetry is the art and science of making accurate
measurements by means of aerial photography:
 Analog photogrammetry (hard -copy photos)
 Digital photogrammetry (digital images)
 Aerial photographs were the first form of remote sensing imagery.
 Differences between photogrammetry and Remote Sensing are that
photographs are:
 Black and white (1 band) or color (blue, green, red, an d IR)
 Wavelength range of 0.3 -1.0 m
 Use cameras
 One type of remote sensing imagery
 Science (or art) of deducing the physical dimensions of objects from
measurements on photographs
 Mapping the earth or other bodies in the solar system
 Sometimes used t o indirectly measure the geometry of buildings,
dams, archeological sites using photographs.
 Sometimes the same principles are applied to digital imagery from
satellite -based RS platforms.
 Science (or art) of deducing the physical dimensions of objects f rom
measurements on photographs
 Mapping the earth or other bodies in the solar system
 Sometimes used to indirectly measure the geometry of buildings,
dams, archeological sites using photographs.
 Sometimes the same principles are applied to digital image ry from
satellite -based RS platforms.
PHOTOGRAMMETRY IS THE TECHNIQUE OF MEASURING
OBJECTS (2D OR 3D) FROM PHOTOGRAPHS. munotes.in

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49 Its most important feature is the fact, that the objects are measured
without being touched .
Objects are measured without touching.
 It is a Remote sensing technique.
 It is a close -range method of measuring objects.
 It is a 3 -dimensional coordinate measuring technique that uses
Photographs as the fundamental medium for measurement.
 Modern Photogrammetry also uses radar imaging, radiant
electromagnetic energy detection and x -ray imaging – called remote
sensing.

Has many uses
Very economical as opposed to on site surveying

The main principle is “TRIANGULATION”.
 Eyes use the principle of TRIANGULATION to gauge distance
(depth perce ption).
 TRIAGULATION is also the principle used by theodolites for
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50  By taking photographs from at least two different locations, so -called
"lines of sight" can be developed from each camera to points on the
object. These lines of s ight (sometimes called rays owing to their
optical nature) are mathematically intersected to produce the 3 -
dimensional coordinates of the points of interest.
3.4.1 Vertical aerial photograph Relief and tilt displacement
Stereoscopy, parallax Equation; flig ht planning Scale and height
determination.
Introduction
Relief displacement is the radial distance between where an object appears
in an image to where it actually should be according to a Planimetric
coordinate system. The images of ground positions are shifted or displaced
due to terrain relief, in the central projection of an aerial photograph. If a
photograph is truly vertical, the displacement of images is in a direction
radial from the photograph center. This displacement is called the radial
displa cement due to relief. Radial displacement due to relief is also
responsible for scale differences within any one photograph, and for this
reason a photograph is not an accurate map. Relief displacement is caused
by differences in relative elevation of obje cts photographed. All objects
that extend above or below a datum plane have their photographic images
displaced to a greater or lesser extent. This displacement occurs always
along the line which connects the photo point and the nadir and is,
therefore ter med “radial line displacement”. Or this displacement is always
radial with respect to principal point. It increases with increasing height of
the feature and the distance from nadir


Vertical aerial photograph of
Long Beach, California, showing
relief displacement (A) Geometry of displacement due to
topographic relief (B)

shows the geometry of image displacement, where light rays are traced
from the terrain through the camera lens and onto the film. Prints made
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51 plane of photographic print in Fig. A. The vertical arrows on the terrain
represent objects of various heights located at various distances from the
principal point. The light ray reflected from the base of object A interse cts
the plane of the photographic print at position A, and the ray from the top
(or point of the arrow) intersects the print at A’. The distance A -A’ is the
relief displacement (d) shown in the plan view in Fig. B.
Geometry of relief displacement on a vert ical aerial photograph
The effect of relief displacement on a photograph taken over varied terrain.
In essence, an increase in the elevation, of a feature causes its position on
the photograph to be displaced radically outward from the principal point.
Hence, when a vertical feature is photographed, relief displacement causes
the top of the feature to lie farther from the photo center than its base. As a
result, vertical feature appears to lean away from the center of the
photograph. The amount of displac ement increases at greater radial
distances from the centre and reaches a maximum at the corners of the
photograph

Flight planning Scale and height determination
The scale of an aerial photograph depends on the specific camera
characteristics (focal leng th) and the flying height at which the image was
captured. There are several methods for calculating the scale of an aerial
photo. Which method you use depends on what information is already
known.
Focal Length and Field of View
The scale of a photograph i s determined by the focal length of the camera
and the flying height above the ground. The focal length is the distance
from the middle of the camera lens to the focal plane. Focal length is
precisely measured when cameras are calibrated and is typically e xpressed
inn millimeters (mm). The focal length of a lens determines the
magnification and the angle of the light ray. The longer the focal length,
the greater the magnification of the image. Short focal length lenses cover
larger areas. The area captured by a camera is known as the Field of View
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52 of the focal length of the lens and the size (sometimes called format) of
digital sensors.

Shorter focal lengths have wider field of vi ews, while longer focal lengths
have smaller field of views. There fore a camera lens with a longer focal
length will produce an image with a smaller footprint compared to that of a
shorter focal length.
The scale of a photo is equal to the ratio between t he camera's focal length
and the plane's altitude above the ground level (AGL) being photographed.
If the focal length and flying altitude above the surface is known, the scale
can be calculated using the following formula:

Flying Height Above Ground Lev el (AGL) vs Above Mean Sea Level
(MSL)
In all of the scale calculations, it is important to know the flying height
above the surface or above ground level (AGL). Sometime the altitude
above sea level or MSL is given and you may need to estimate the average
flying height above ground. For example, the GSP on a unmanned aerial
vehicle (UAV) may record the altitude or height above sea level and not
above ground level (AGL). To estimate the AGL, you will need to
determine the average elevation of the terrain an d subtract that from
altitude above sea level. This will give you the average flying height above
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53

Multiple Choice Questions.
1. The lens used in aerial photogrammetry is having a maximum
coverage capacity of _________ (in angles)
a) 930
b) 630
c) 530
d) 980
2. Which of the following is not a type of shutter used in aerial
photogrammetry?
a) Between -the-lens shutter
b) Louvre shutter
c) Ideal shutter
d) Focal plane shutter
3. For placing focal plane, which is used as a reference?
a) Focal length
b) Horizon
c) Azimuth
d) Collimation marks
4. Focal plane varies while aerial photogrammetry is carried out.
a) True
b) False
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54 5. Which among the following surveying methods is meant to be having
high precision?
a) Aerial photogrammetry
b) Terrestrial photo grammetry
c) Theodolite surveying
d) Traverse surveying
6. Vertical photograph coincides with the__________
a) Direction of line of sight
b) Direction of lens
c) Direction of aperture
d) Direction of gravity
7. How much inclination must be provided in a ti lted photograph?
a) 13 ˚
b) 20 ˚
c) 3˚
d) 34 ˚
8. If the apparent horizon is shown in a photograph, it is low oblique.
a) True
b) False
9. Perspective projection is produced from__________
a) Straight lines radiating a common point
b) Straight lines radiating different points
c) Parallel lines radiating a common point
d) Perpendicular lines radiating a common point
10. Flying height refers to_________
a) Upper portion of the exposure station
b) Bottom of the exposure station
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55 Answer
Q. No. 01
Answer: a
Explanation: In general, the lens used in aerial photogrammetry having a
minimum coverage area of 630 and a maximum coverage area of 930. The
usage of the coverage angle depends upon the type f land being surveyed
and the accuracy needed in output.
Q. No. 02
Answer: c
Explanation: Shutter plays a prominent role in the process of aerial
photogrammetry. The speed of shutter must be in such a way that it should
function at a speed of 1/100 to 1/1000 second. I t is classified as between
the lens type, focal plane type, Louvre type.
Q. No. 03
Answer: d
Explanation: Collimation marks can be used as a reference while placing
the focal plane. It may place the focal plane at a near distance from nodal
plane from whic h the best possible image can be obtained.
Q. No. 04
Answer: b
Explanation: In the process of aerial photogrammetry, the air -craft is
placed at a considerable height so that it can cover a huge area while
taking photographs. But the focal plane of the aeri al camera is fixed at one
location, rather than varying.
Q. No. 05
Answer: a
Explanation: Though terrestrial photogrammetry is having accuracy in the
obtained values, aerial photogrammetry is capable of producing precise
output when compared to the remaini ng methods. This accuracy makes it
different from the remaining methods and is recommended when high
quality works are conducted.
Q. No. 06
Answer: d
Explanation: The aerial photograph consists of a vertical photograph
which is made of the camera axis whic h is made to coincide with the
direction of gravity. Optical axis must be first made straight in order to
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56 Q. No. 07
Answer: c
Explanation: In general, a tilted photograp h consists of inclination up to 3 ˚,
which makes it to have an individual tilted scale. It might help in
determining the objects which are inclined in the photograph.
Q. No. 08
Answer: b
Explanation: Oblique photograph is used in case of aerial photography,
with an intention that the camera axis lies in between horizontal and
vertical. High oblique is obtained in case of possessing apparent horizon
otherwise it isn’t shown.
Q. No. 09
Answer: a
Explanation: The introduction of perspective projection is done b y the
straight lines radiating a common point and passing through point on the
spherical surface. Aerial photogrammetry uses this phenomenon.
Q. No. 10
Answer: d
Explanation: Flying height indicates the elevation of the exposure station
above the sea level. Any datum selected can act as a reference so that the
flying height can be considered from them.
References
[1] Thomas M. Lillesand and Ralph W. Kiefer: Univer sity of Wisconsin -
Madison, Third Edition, Remote Sensing and Image Interpretation.
[2] Floyd F. Sabins, Jr., Chevron Oil Field Research Company and
University of California, Los Angeles, Second Edition, Remote
Sensing: Principles and Interpretation.
[3] Kimerling, A. Jon, Muehrcke, Juliana O. (2005). Map Use Reading
Analysis Interpretation, Fifth Edition. JP Publications.
[4] Jensen, J.R. 2007. Remote Sensing of the Environment: An Earth
Resource Perspective. Pearson Prentice Hall.
[5] Wolf, P.R. 1974. Elements of Photogrammetry, McGraw -Hill, Inc.
[6] Pateraki, M.2006. Digital Aerial Cameras. International Summer

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57 4
APPLICATIONS OF REMOTE SENSING
TECHNIQUES IN GEOGRAPHICAL
STUDIES
Unit Structure:
4.1 Objectives
4.2 Introduction
4.3 Subject Discussion
4.4 Principles and fundamentals of aerial photo interpretation
4.5 Image analysis Elements, Fundamentals of satell ite images
analysis: Types of Imagery, Visual image analysis, digital image
analysis
4.6 Basic principles of thermal and microwave remote sensing
4.7 Principles of Microwave Remote Sensing
4.8 Summary
4.9 Check your Progress/Exercise
4.10 Answers to Se lf-Learning Questions
4.11 Technical Words and their meaning
4.12 Tasks
4.13 Reference for further reading/study
4.1 OBJECTIVES
By the end of this unit, you will be able to -
 Know about the principles and fundamentals of aerial photo
interpretation
 Expl ain the elements of image analysis, fundamentals of satellite
image analysis
 Learn about the types of imagery, visual image analysis and digital
image analysis
 Understand the principles of thermal remote sensing
 Perceive the role of microwave remote sensin g

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58 4.2 INTRODUCTION
The chapter will deal with aerial photo interpretation and its fundamentals,
principles, and elements. Besides, it will also focus on visual and digital
image analysis which are fundamentals to aerial photos. It will enhance
the know ledge regarding thermal and microwave remote sensing. This
chapter will help to provide an insight on the basics of how the aerial
photo interpretations and remote sensing is carried out.
4.3 SUBJECT DISCUSSION
Analysis of remote sensing imagery involves the identification of various
targets in an image, and those targets may be environmental or artificial
features which consist of points, lines, or areas. Targets may be defined in
terms of the way they reflect or emit radiation. This radiation is measured
and recorded by a sensor, and ultimately is depicted as an image product
such as an air photo or a satellite image.What makes interpretation of
imagery more difficult than the everyday visual interpretation of our
surroundings? For one, we lose our sense of depth when viewing a two -
dimensional image, unless we can view it stereoscopically to simulate the
third dimension of height. Indeed, interpretation benefits greatly in many
applications when images are viewed in stereo, as visualization (and
therefore, recognition) of targets is enhanced dramatically. Viewing
objects from directly above also provides a very different perspective than
what we are familiar with. Combining an unfamiliar perspective with a
very different scale and lack of recognizable detai l can make even the
most familiar object unrecognizable in an image. Finally, we are used to
seeing only the visible wavelengths, and the imaging of wavelengths
outside of this window is more difficult for us to comprehend.
4.4 FUNDAMENTALS OF AERIAL PHOTO GRAPHIC
INTERPRETATION
Photo Interpretation: The examination of aerial photographs/images for
the purpose of identifying objects and judging their significance.

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59 Observation & Inference: Observation provides the raw data for
interpretation. Inference is the logical process by which observation and
interpretation are made.



Source:https://www.nrcan.gc.ca/
4.5 IMAGE ANALYSIS ELEMENTS, FUNDAMENTALS
OF SATELLITE IMAGES ANALYSIS: TYPES OF
IMAGERY, VISUAL IMAGE ANALYSIS, D IGITAL
IMAGE ANALYSIS
Image analysis Elements:
Recognizing targets is the key to interpretation and information extraction.
Observing the differences between targets and their backgrounds involves
comparing different targets based on any, or all, of the v isual elements
of tone, shape, size, pattern, texture, shadow, and association . Visual
interpretation using these elements is often a part of our daily lives,
whether we are conscious of it or not. Examining satellite images on the
weather report, or follo wing high speed chases by views from a helicopter
are all familiar examples of visual image interpretation. Identifying targets
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60 further interpret and analyse. The nature of each of these interpretation
elements is described below, along with an image example of each.

Source:https://www.nrcan.gc.ca/
Tone refers to the relative brightness or colour of objects in an image.
Generally, tone is the fundamental element for distinguishing betwe en
different targets or features. Variations in tone also allows the elements of
shape, texture, and pattern of objects to be distinguished.

Source:https://www.nrcan.gc.ca/
Shape refers to the general form, structure, or outline of individual objects.
Shape can be a very distinctive clue for interpretation. Straight edge
shapes typically represent urban or agricultural (field) targets, while
natural features, such as forest edges, are generally more irregular in
shape, except where man has created a road or clear cuts. Farm or crop
land irrigated by rotating sprinkler systems would appear as circular
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61

Source:https://www.nrcan.gc.ca/
Size of objects in an image is a function of scale. It is important to assess
the size of a target relative to other objects in a scene, as well as the
absolute size, to aid in the interpretation of that target. A quick
approximation of target size can direct interpretation to an appropriate
result more quickly. For example, if an interpreter had to distinguish zones
of land use, and had identified an area with a number of buildings in it,
large buildings such as factories or warehouses would suggest commercial
property, whereas small buildings would indicate residential use.

Source:https://www.nrcan.gc.ca/
Pattern refers to the spatial arrangement of visibly discernible objects.
Typically, an orderly repetition of similar tones and textures will produce
a distinctive and ultimately recognizable pattern. Orchards with evenly
spaced trees, and urban streets with regularl y spaced houses are good
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62

Source:https://www.nrcan.gc.ca/
Texture refers to the arrangement and frequency of tonal variation in
particular areas of an image. Rough textures would consist of a mottled
tone where the grey levels change abruptly in a small area, whereas
smooth textures would have very little tonal variation. Smooth textures are
most often the result of uniform, even surfaces, such as fields, asphalt, or
grasslands. A target with a rough surface and irregular structure, su ch as a
forest canopy, results in a rough textured appearance. Texture is one of the
most important elements for distinguishing features in radar imagery.

Source:https://www.nrcan.gc.ca/
Shadow is also helpful in interpretation as it may provide an idea of the
profile and relative height of a target or targets which may make
identification easier. However, shadows can also reduce or eliminate
interpretation in their area of influence, since targets within shadows are
much less (or not at all) discernible from their surroundings. Shadow is
also useful for enhancing or identifying topography and landforms,
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63

Source:https://www.nrcan.gc.ca/
Association considers the relationship between other recognizable objects
or features in proximity to the target of interest. The identification of
features that one would expect to associate with other features may
provide information to facilitate identification. In the example given
above, commercial properties may be associated with proxim ity to major
transportation routes, whereas residential areas would be associated with
schools, playgrounds, and sports fields. In our example, a lake is
associated with boats, a marina, and adjacent recreational land.
4.5 FUNDAMENTALS OF SATELLITE IMAGES
ANALYSIS: TYPES OF IMAGERY, VISUAL
IMAGE ANALYSIS, DIGITAL IMAGE ANALYSIS

Remotely sensed satellite data comes in two basic types, passively
collected data, and actively collected data.
1. Passive data collection focuses on acquiring intensities of
electr omagnetic radiation generated by the sun and reflected off the
surface of the planet.
2. Active data collection is largely restricted to devices that send and
generate a pulse of energy to that is reflected to the satellite to be
recorded. Most of the readil y available data is passively collected and
is limited to energy not absorbed by the Earth's atmosphere.
3. Satellite imagery based on passive reflectivity comes in 4 basic types,
which are visible, infrared, multispectral, and hyperspectral.
The type and re solution of the data that is collected is generally keyed to
the mission of the satellite.
1. Visible data consists of pixels composed of colour values of red,
green, and blue to make three bands of data on a raster image.
2. Infrared imagery usually consists o f the images that include the
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64

Source: https://www.satimagingcorp.com/gallery/quickbird/q uickbird -oil-
and-gas-near-infrared/
3. Multispectral data include up to 7 -12 channels of data

Source: https://www.mdpi.com/2072 -4292/14/13/3046/htm
4. Hyperspectral can be up to 50 bands or more of data collected over
discrete bandwidths of the electromagnet ic spectrum.
How all this data is used goes beyond the scope of this site, but it's worth
keeping in mind that there are a range of available products and it may
require a great deal of research to determine what type of data is useful in
the context of t he field -based exercise.
 Image Interpretation
Image interpretation is defined as the extraction of qualitative and
quantitative information in the form of a map, about the shape, location,
structure, function, quality, condition, relationship of and betwee n objects,
etc. by using human knowledge or experi ence. As a narrow definition,
"photo -interpretation " is sometimes used as a synonym of image
interpretation.
Image interpretation in satellite remote sensing can be made using a single
scene of a satellite image, while usually a pair of stereoscopic aerial munotes.in

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65 photographs are used in photo -interpretation to provide stereoscopic vision
using, for example, a mirror stereoscope. Such a single photo -
interpretation is discriminated from stereo photo -interpretation.

Source: http://sar.kangwon.ac.kr/etc/rs_note/rsnote/cp7/cp7 -2.htm
 Image reading is an elemental form of image interpretation. It
corresponds to simple identification of objects using such elements as
shape, size, pattern, tone, texture, colour, shadow and other associated
relationships. Image reading is usually implemented with interpretation
keys with respect to each object.
 Image measurement is the extraction of physical quantities, such as
length, location, height, density, temperature and so on, by usi ng
reference data or calibration data deductively or inductively.
 Image analysis is the understanding of the relationship between
interpreted information and the actual status or phenomenon, and to
evaluate the situation.Extracted information will be final ly represented
in a map form called an interpretation map or a thematic map.
Generally, the accuracy of image interpretation is not adequate without
some ground investigation. Ground investigations are necessary, first
when the keys are established and the n when the preliminary map is
checked. Analysis of remote sensing imagery involves the identification of
various targets in an image, and those targets may be environmental or
artificial features which consist of points, lines, or areas. Targets may be
defined in terms of the way they reflect or emit radiation. This radiation is
measured and recorded by a sensor, and ultimately is depicted as an image
product such as an air photo or a satellite image. munotes.in

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66  What makes interpretation of imagery more difficult than the everyday
visual interpretation of our surroundings? For one, we lose our sense of
depth when viewing a two -dimensional image, unless we can view
it stereoscopically to simulate the third dimension of height. Indeed,
interpretation benefits greatly in m any applications when images are
viewed in stereo, as visualization (and therefore, recognition) of targets
is enhanced dramatically. Viewing objects from directly above also
provides a very different perspective than what we are familiar with.
Combining a n unfamiliar perspective with a very different scale and
lack of recognizable detail can make even the most familiar object
unrecognizable in an image. Finally, we are used to seeing only the
visible wavelengths, and the imaging of wavelengths outside of t his
window is more difficult for us to comprehend.
Visual Image Analysis:
Images should be analysed evaluated on several levels. Visual analysis is
an important step in evaluating an image and understanding its meaning. It
is also important to consider tex tual information provided with the image,
the image source and original context of the image, and the technical
quality of the image.

Source:https://www.researchgate.net/figure/1 -Elements -of-remote -
sensing -Lillesand -and-Kiefer -1994_fig1_3298633
Digital Image Analysis:
In today's world of advanced technology where most remote sensing data
are recorded in digital format, virtually all image interpretation and
analysis involve some element of digital processing. Digital image
processing may involve numerous procedures including formatting and
correcting of the data, digital enhancement to facilitate better visual
interpretation, or even automated classification of targets and features
entirely by computer. In order to process remote sensing imagery digitally ,
the data must be recorded and available in a digital form suitable for
storage on a computer tape or disk. Obviously, the other requirement for
digital image processing is a computer system, sometimes referred to as
an image analysis system, with the app ropriate hardware and software to
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67 been developed specifically for remote sensing image processing and
analysis.
For discussion purposes, most of the common image processing functions
available in image analysis systems can be categorized into the following
four categories:
 Pre-processing
 Image Enhancement
 Image Transformation
 Image Classification and Analysis
Pre-processing functions involve those operations that are normally
required pr ior to the main data analysis and extraction of information, and
are generally grouped as radiometric or geometric corrections.
Radiometric corrections include correcting the data for sensor
irregularities and unwanted sensor or atmospheric noise, and conv erting
the data so they accurately represent the reflected or emitted radiation
measured by the sensor. Geometric corrections include correcting for
geometric distortions due to sensor -Earth geometry variations, and
conversion of the data to real world coo rdinates (e.g. latitude and
longitude) on the Earth's surface.

Source:https://www.nrcan.gc.ca/
The objective of the second group of image processing functions grouped
under the term of image enhancement, is solely to improve the appearance
of the imager y to assist in visual interpretation and analysis. Examples of
enhancement functions include contrast stretching to increase the tonal
distinction between various features in a scene, and spatial filtering to
enhance (or suppress) specific spatial patterns in an image.
Image transformations are operations similar in concept to those for image
enhancement. However, unlike image enhancement operations which are
normally applied only to a single channel of data at a time, image
transformations usually involve combined processing of data from
multiple spectral bands. Arithmetic operations (i.e. subtraction, addition,
multiplication, division) are performed to combine and transform the
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68 features in the scene. We will look at some of these operations including
various methods of spectral or band ratioing, and a procedure
called principal components analysis which is used to represent the
information more efficiently in multichannel imagery.

Source: https://www.mdpi.com/2072 -4292/14/13/3046/htm
Image classification and analysis operations are used to digitally identify
and classify pixels in the data. Classification is usually performed on
multi -channel data sets (A) and this process assigns each pixel in an image
to a particular class or theme (B) based on statistical characteristics of the
pixel brightness values. There are a variety of approaches taken to perform
digital classification. We will briefly describe the two generic approaches
which are used most often, namely supervised, and unsupervised
classification.
4.6 BASIC PRINCIPLES OF THERMAL AND
MICROWAVE REMOTE SENSING
Principles of Thermal Remote Sensing
The earth -atmosphere system derives its energy from the sun which being
at a ve ry high temperature, radiates maximum energy in the shorter
wavelengths (visible, 0.20 to 0.80 mm). The earth -atmosphere system
absorbs part of this energy (part due to its reflective properties due to
surface albedo, clouds, and other reflectors/scatterer s in the atmosphere),
which in turn heats it up and raises its temperature. This temperature being
in the range of 300 degrees Kelvin, it will emit its own radiation in the
longer wavelengths called 'thermal infrared'. The observations in the
thermal wavel ength of the electromagnetic spectrum (3 -35 mm) are
generally referred to as thermal remote sensing.
In this region the radiation emitted by the earth due to its thermal state are
far more intense than the solar reflected radiation, therefore any sensor
operating in this wavelength region would primarily detect the thermal
radiative properties of ground material. All materials having a temperature
above absolute zero ( -273°C or 0°K) both day and night emit Infrared
energy Infrared sensing refers to the det ection of remote objects by
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69 continuous tone image on photographic film. Thermal IR imagery is
usually obtained in the wavelength regions 3 to 5.5mm and from 8 to
14mm because of atm ospheric absorption at other wavelengths.
IR Region of the Electromagnetic Spectrum the IR region covers
wavelengths from 0.7 to 300mm. The reflected IR region ranges from
wavelengths 0.7 to 3 mm and includes the photographic IR band (0.7 to
0.9 mm) that may be detected from IR film. IR radiation at wavelengths 3
to 14mm is called the thermal IR region. Since thermal IR radiation is
absorbed by glass lenses of conventional cameras and cannot be detected
by photographic film.
Special optical mechanical sca nners are used to detect and record images
in the thermal IR region. IR radiation at wavelengths larger than 14mm is
not utilized in remote sensing as the radiation is absorbed by the earth's
atmosphere.
Atmospheric transmission Thermal sensing of solids and liquids occurs in
two atmospheric windows, where absorption is a minimum, as shown in
this spectral plot taken from Sabin’s (Remote Sensing: Principles and
Interpretation, 1987).

Source: http://sar.kangwon.ac.kr/etc/rs_note/rsnote/cp7/cp7 -2.htm
Not all wavelengths of thermal IR radiation are transmitted uniformly
through the atmosphere CO2, Ozone and water vapor absorb energy at
certain wavelengths IR and radiation at wavelengths from 3 -5 mm and
from 8 -14 mm is readily transmitted through the atmosph ere windows. A
narrow absorption band occurs from 9 -10 mm occurs due to the ozone
layer present at the top of the earth's atmosphere.
To avoid the affected of this absorption band, satellite thermal IR systems
operate in 10.5 - 12.5mm. Systems on aircraft flying below the ozone
layer are not affected and record the full 8 -14mm band.
The windows normally used from aircraft platforms are in the 3 -5 mm and
8-14 µm wavelength regions. Space borne sensors commonly use
windows between 3 and 4 µm and between 10. 5- 12.5 µm. None of the
windows transmits 100 percent because water vapor and carbon dioxide
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70 in the 10.5 -12.5 µm interval. In addition, solar reflectance contaminates
the 3 -5-µm windows to some degree during daylight hours, hence is used
for Earth studies using night -time measurements.

Source: http://sar.kangwon.ac.kr/etc/rs_note/rsnote/cp7/cp7 -2.htm
4.7 PRINCIPLES OF MICROWAVE REMOTE SENSING
Microwave remote sensing, using microwave r adiation using wavelengths
from about one centimetre to a few tens of centimetres enables
observation in all weather conditions without any restriction by cloud or
rain. This is an advantage that is not possible with the visible and/or
infrared remote sens ing. In addition, microwave remote sensing provides
unique information on for example, sea wind and wave direction, which
are derived from frequency characteristics, Doppler effect, polarization,
back scattering etc. that cannot be observed by visible and infrared
sensors. However, the need for sophisticated data analysis is the
disadvantage in using microwave remote sensing.
There are two types of microwave remote sensing; active and passive.
The active type receives the backscattering which is reflected f rom the
transmitted microwave which is incident on the ground surface.
Synthetic aperture radar (SAR), microwave scatter -o-meters, radar
altimeters etc. are active microwave sensors. The passive type receives the
microwave radiation emitted from objects on the ground. The microwave
radiometer is one of the passive microwave sensors. The process used by
the active type, from the transmission by an antenna, to the reception by
the antenna is theoretically explained by the radar equation as described
in Figure 3.1.1 . munotes.in

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71

Source: http://sar.kangwon.ac.kr/etc/rs_note/rsnote/cp7/cp7 -2.htm
The process of the passive type is explained using the theory of radiative
transfer based on the law of Rayl eigh Jeans as explained in Figure 3.1.2 .

Source:https://www.nrcan.gc.ca/
In both active and passive types, the sensor may be designed considering
the optimum frequency needed for th e objects to be observed.
In active microwave remote sensing, the characteristics of scattering can
be derived from the radar cross section calculated from received power Pr
and antenna parameters (At,Pt, Gt ) and the relationship between them,
and the ph ysical characteristics of an object. For example, rainfall can be
measured from the relationship between the size of water drops and the
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Source:https://www.nrcan.gc.ca/
In passive microwave remote sensing, the characteristics of an object can
be detected from the relationship between the received power and the
physical characteristics of the object such as attenuation and/or radiation
characteristics.

Source:https://www.nrcan.gc.ca/
4.8 SUMMARY
Let us now summarise what has been discussed in this unit:
 Image interpretation is the process of extraction of information both
qualitative and quantitative from aerial photographs and satellite
images in the form of a map. This technique is used to collect
information for a variety of pu rposes.
 Image interpretation is carried out either manually or with the help of
computer software and is known as visual and digital interpretation,
respectively.
 Visual interpretation is a process of identifying features seen on
photographs/images and c ommunication of information obtained from
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73  The information extraction from aerial data (i.e., photo interpretation)
is based on the characteristics of photograph features, such as size,
shape, tone, texture , shadow, pattern, and association. The basic
elements of visual image interpretation are like those used in aerial
photo interpretation.
 The criteria for identification of an object with interpretation elements
are called an interpretation key.
4.9 CHECK YOUR PROGRESS/EXERCISE

1. Identify the tone of the given image?
2. Find the texture of the features available in the image.
3. Identify the shapes depicted in the given image.
4. Describe the association of the image and the given sizes of the
features.
5. Explain the pattern of the features viewed in the image.
6. Define the site captured in the image.
4.10 ANSWERS TO SELF -LEARNING QUESTIONS
Mention your answers in the given format:


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74 4.11 TECHNICAL WORDS AND THEIR MEANING
 band: The information stored in one raster, often recording a specific
bandwidth of the electromagnetic spectrum. An image may be
composed of one or more bands.
 classification scheme: A set of class categories to which image pixels
or objects will be assigned. Appropriate classification schemes
depend on the type of sensors utilized, their resolution, feature types
of interest and the biome or landcover / land use imaged.
 classification: The computational process of assigning pixels or
objects into a set of categories, or classes, having common spe ctral,
shape, elevation or other definable characteristics.
 collection characteristics: Attributes describing how imagery was
collected, including spectral, radiometric, spatial, and temporal
resolutions, viewing angle, and extent.
 colour (image element): characteristic of an object of interest derived
from combinations of the red, green, and blue spectral bands of
imagery, used to help identify the object
 electromagnetic energy: Energy (like that emitted from the sun) that
moves through space at the speed of light at different wavelengths.
Types of electromagnetic radiation include gamma, x, ultraviolet,
visible, infrared, microwave, and radio.
 image elements: all the characteristics of an image, including its
tone/colour, shape, size, pattern, shadow, tex ture, location, context,
height, and date
 image filter: On a raster, an analysis boundary or processing window
within which cell values affect calculations and outside which they do
not. Filters are used mainly in cell -based analysis where the value of a
centre cell is changed to the mean, the sum, or some other function of
all cell values inside the filter. A filter moves systematically across a
raster until each cell has been processed. Filters can be of various
shapes and sizes, but three -cell by three -cell squares are common.
 insolation: the amount of solar radiation received by an area over a
given period of time
 location (image element): the x, y, and z coordinates of an object of
interest, used to help identify the object.
 pattern (image element): the spatial arrangement or configuration of
objects, used to identify an object of interest.
 scale: The ratio or relationship between a distance or area on a map
and the corresponding distance or area on the ground, commonly
expressed as a fraction or ratio. A map scale of 1/100,000 or
1:100,000 means that one unit of measure on the map equals 100,000
of the same unit on the earth.
 shadow (image element): the consequence when the sensor’s ability
to capture reflectance or radiance of a feature on the ground is
hindered by another feature; used to help identify objects of interest
 shape (image element): the form of the outline of an object of
interest, used to help identify the object
 size (image element): the extent of an object of interest, used to help
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75  slope: The incline, or steepness, of a surface, measured in degrees
from horizontal (0 –90), or percent slope (the rise divided by the run,
multiplied by 100). The slope of a TIN face is the steepest downhill
slope of a plane defined by the face; the slope for a cell in a raster is
the steepest slope of a plane defined by the cell and its eight
surrounding neighbours.
 temporal resolution: The frequency at which images are captured
over the same location on the earth's surface.
 texture (image element): the feel or appearance of the surface of an
object of interest, used to help identify the object
 tone (image element): characteristic of an object of interest derived
from the intensity of spectral response in each band of an image, used
to help identify the object
 topography: The study and mapping of land surfaces, including relief
(relative positions and elevations) and the position of natural and
constructed features.
4.12 TASKS
1) Discuss in brief the elements of visual image interpretation.
2) What do you understand by image interpretation keys?
3) What is the importance of scale in image interpretation?
4.13 REFERENCE FOR FURTHER READING/STUDY
 Jensen, John R. (2000). Remote Sensing of the Environment. Prentice
Hall. ISBN 978-0-13-489733 -2.
 Olson, C. E. (1960). "Elements of photographic interp retation
common to several sensors". Photogrammetric Engineering. 26 (4):
651–656.
 Philipson, Warren R. (1997). Manual of Photographic
Interpretation (2nd ed.). American Society for Photo grammetry and
Remote Sensing. ISBN 978 - 1- 57083 - 039 - 6.
 Remote Sensing and GIS by Basudeb Bhatta, Oxford Publ ication
 Fundamentals of Remote Sensing by George Joseph and C
Jeganathan, Universities Press
 https://link.springer.com/book/10.1007/978 -981-16-7731 -1
 https://gisgeography.com/remote -sensing -earth -observation -guide/
 https://www.nic.in/servicecontents/remote -sensing -gis/
 https://www.springer.com/journal/12524
 https://www.nrsc.gov.in/
 https://www.nasa.gov/ munotes.in

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76  https://www.edc.uri.edu/nrs/classes/NRS409509/RS/Lectures/409509
PhotoInterpretationClass3_Update.pdf
 https://www.nrcan.gc.ca/maps -tools -and-publications/satellite -
imagery -and-air-photos/tutorial -fundamentals -remote -sensing/image -
interpretation -analysis/elements -visual -interpretation/9291
 https://egyankosh.ac.in/bitstream/123456789/39535/1/Unit -7.pdf
 https://www.icao.int/APAC/Meetings/2016%20WMOICAOSIGMET/
Report_Attachment -D3_JMA -satellite -image -analysis.pdf
 http://sar.kangwon.ac.kr/etc/rs_note/rs note/contents.htm
 http://gsp.humboldt.edu/olm/Courses/GSP_216/lessons/thermal/
 https://webapps.itc.utwente.nl/librarywww/papers_2009/general/princ
iplesremotesensing.pdf
 https://www.lkouniv.ac.in/site/writereaddata/ siteContent/2020040219
10157352ajay_misra_geo_Thermal_RS.pdf
 https://www.mlsu.ac.in/econtents/455_Thermal%20Remote%20Sensi
ng%20and%20its%20applications.pdf


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