On Remote Sensing Basics

1. DR. ABHISHEK PANDEY
Exploration Division
Remote Sensing Concepts and Basics of
Image Processing and Analysis

2. Learning Objectives
Brief overview of concepts related to remote sensing of features on the
land surface:
• Electromagnetic radiation (EMR) and its interactions with
atmosphere and materials
• Spectral signatures
• Concept of a digital image
• Image acquisition : spatial, spectral and radiometric resolutions
• Image analysis and processing.

3. Background
Remote sensing: obtaining information about the earth’s
surface by recording reflected or emitted energy by
sensors, and processing, analyzing, and applying that
information
Satellite imageries: processed remote sensing data for
mapping, monitoring and managing earth’s surface
features – input to GIS
Interpretation and analysis of imageries: based on
relationships between properties measured by sensors
and properties of land surface.

4. Electromagnetic Spectrum
Regions of relevance to remote
sensing:
• visible: 0.4 to 0.7 micrometers
• infrared: 0.7 to 1000 micrometers
• microwave: 0.1 to 100 cm

5. Matter-Energy (EMR) Interaction
•Transmission: EMR passes through a
material with little change in intensity
• Absorption: EMR that is absorbed is
transformed into heat energy which raises
the material’s temperature Absorption is
wavelength and material specific
• Emission: Absorbed heat energy is
emitted as EMR at a wavelength
dependent on material’s temperature.
(lower temperature longer
wavelength of emitted EMR).
• Reflection: smooth surface reflection is
specular reflection; Rough surfaces cause
scattering

6. EMR - Atmosphere – Earth surface
Interactions

7. EMR and Atmosphere Interactions -
Concept of Atmospheric window
• water vapour, carbon dioxide and ozone are significant absorbers of
EMR in specific wave-length ranges
• some wavelengths are almost completely absorbed
• wavelength ranges (wavelength bands) with high transmission values
(minimal absorption) are suitable for use in remote sensing to detect
and measure EMR from land surface features - atmospheric windows
• choice and design of sensors to detect EMR is based on available
atmospheric windows

8. Atmospheric Windows for remote sensing
Visible to near
infrared:
0.4 - 0.7 micro m
Near infrared:
0.7–1.1micro m
short infrared:
1.1-2.5 micro m
Mid infrared:
3 – 5 micro m
Thermal infrared:
8-14 micro m
Microwave:
1-30 cm

9. EMR interactions with land surface
features – spectral signatures for
feature identification
• Different materials have different patterns of wavelength specific
absorption and reflection of EMR – spectral signatures
• Property used to qualify spectral signatures is spectral reflectance – ratio
of reflected to incident energy as a function of wavelength
• Spectral reflectance of different materials can be measured to provide
reference data to interpret images
• Surface materials can be distinguished from each other by differences in
spectral reflectance

10. Spectral reflectance curves for vegetation, soil
and water

11. Spectral plots for healthy and stressed crops

12. Spectral bands and earth surface features
0.45-0.52
m
Blue Mapping coastal water, soil vegetation
discrimination forest type mapping
0.52-0.60
m
Green Vegetation discrimination and vigor
assessment
0.63-0.69
m
Red Chlorophyl absorption region; plant species
identification
0.76-0.90
m
Near
Infrared
Determination of vegetation types, vigor,
biomass content; also delineation of water
bodies and soil moisture
1.55-1.75
m
Mid
Infrared
Plant moisture content and soil moisture;
also differentiate snow from clouds
2.08-2.35
m
Mid
Infrared
Discrimination of mineral and rock types.
Sensitive to plant moisture content
10.4-12.5 Thermal
Infrared
Vegetation moisture content, soil moisture
discrimination, and thermal mapping
applications

13. Spectral bands and earth
surface features
• photograph is scanned and
subdivided into pixels
• each pixel is assigned a digital
number based on its relative
brightness
Basis for assigning numbers:
• computers understand binary
numbers
• a string of 8 binary numbers
is used to represent each
shade
• numbers range from:
00000000 (darkest) to
11111111 (brightest)
• a total of 256 shades of gray
between black and white can be
distinguished
Fig Source: Canada Centre for Remote Sensing

14. Satellite Image acquisition - overview

15. The imaging systems, swath and resolution
• linear detector array with a number of detector elements
• each detector element projects an "instantaneous field of view (IFOV)" on
the ground.
• at any instant, a row of pixels are formed.
• as the detector array flies along its track, the row of pixels
• sweeps along to generate a two-dimensional image.

16. Interpretation of digital numbers
• A digital satellite image is a grid of numbers with values for each
pixel representing the corresponding land surface
• Image is georeferenced to GCPs to associate brightness values
with features on land surface
Fig Source: Canada Centre for Remote Sensing

17. Resolutions of images
• Spatial resolution: ability to distinguish smallest size detail of pattern
on image (relative to ground)
• Spectral resolution: ability to distinguish between signals of different
wavelengths (wavelength bands)
• Radiometric resolution: ability to distinguish between signals of
different strength for the same wavelength band (number of discrete
levels into which electrical signals can be quantized - most satellites
use 256 levels of shades)
• Temporal resolution: time between images of same features of land
surface

18. Spatial Resolution and Map Scale
Spatial resolution Map scale
1m x 1m 1:5000
5m x 5m 1:25000
10m x 10m 1:50000
25m x 25m 1:125000
50m x 50m 1:250000
100m x 100m 1:500000
Basis: 1 pixel
represents
0.2 mm on
map

19. Spatial Resolution and Map Scale
Spatial resolution Map scale
1m x 1m 1:5000
5m x 5m 1:25000
10m x 10m 1:50000
25m x 25m 1:125000
50m x 50m 1:250000
100m x 100m 1:500000
Basis: 1 pixel
represents
0.2 mm on
map

20. Spectral Resolution
• the range of wavelengths that a sensor is able to detect and measure
(number of spectral bands)
• features on the ground (eg water, vegetation) can be identified by the
different wavelengths reflected
• an image produced by a sensor system can comprise:
- one broad wavelength band (panchromatic)
- a few broad bands (multispectral)
- many narrow wavelength bands (hyperspectral)
• most RS satellites use panchromatic and/or multispectral sensors
sensitive to different wavelength bands

21. Multispectral Scanners
• comprise parallel sensor arrays for detecting radiation in a small number
of broad wavelength bands
• most satellite systems use 3 to 6 spectral bands in the visible to
mid-infrared wavelength region
• bands in infrared regions are limited in width to avoid atmospheric
absorption effects
• increased spectral resolution increases ability to classify images
• IRS LISS III MSS uses green (0.52-59), red (0.62-68),
near infrared (0.77-0.86) and mid infrared (1.55- 1.70) bands
.

22. Combining images from different bands to
increase contrast – the false colour
composite (FCC)
• Each band generates a B/W image and each has different contrast
• Eye can distinguish only 20-30 gray tones but over 20000 colour tints
• B/W images in each band are displayed in red, green or blue colours to
achieve relative contrast between the bands

23. Other methods of Image Processing and Analysis
• Band ratios – NIR/RED
• Band Maths
 NDVI : (NIR-RED)/(NIR+RED)
• Band Combination e.g. 432, 654, 752
• Classification (Supervised and Unsupervised)

24. THANKS !