REMOTE SENSING AND IMAGE PROCESSING

BEG……

Year: II Semester: IV

Lectures: 45 Hours
Exercise: 15 Hours
Year: II
Semester: IV
Credit hrs.: 3
Full Marks: 100

Course Objectives

a) To provide general and theoretical knowledge about remote sensing techniques including platforms, sensors, and receiving stations. b) To provide knowledge on panchromatic, infrared, multispectral, thermal, radar, and hyperspectral sensors. c) To provide knowledge on correction of received imageries. d) To train to carry out image mapping including classification and corrections of received data.

Course Contents

1. Electromagnetic Radiation (5 hrs.)

• Introduction
• Electromagnetic Energy
• Waves and photons
• Sources of EM energy (Radiation, emission)
• EM spectrum (Wavelength, Visible bands)
• Interaction of EM energy
• Interaction in atmosphere (Absorption, Transmission, Atmospheric Window)
• Atmospheric scattering (Selective and non-selective)
• Interaction with Earth surface (Reflection, absorption, and transmission, Spectral signature)
• Recording EM Energy
• Multispectral Imageries (Band combination, layer stacking, Natural color, false color, Pseudo Color composite)
• Resolution of images (Spatial, Spectral, Radiometric, and Temporal)

2. Remote Sensing Systems (4 hrs.)

• Sensors
• Active and passive Remote sensing
• Along track and across track scanning
• Platforms
• Types of platforms (Terrestrial, Aerial, Space-based)
• Orbits and viewing angles (Geo-stationary, Sun synchronous, Pole viewing, Pole non-viewing, Repeat and Revisit)

3. Microwave, Thermal, Weather, and Hyperspectral Satellites (4 hrs.)

• Microwave Remote Sensing
  • Working principle
  • SAR, SLAR, InSAR
  • Error, Correction, and application
• Thermal Remote Sensing
  • Working principle
  • Application
• Weather Satellites
  • Working principle
  • Application
• Hyperspectral Satellites
  • Working principle
  • Application

4. Image Radiometric Correction (4 hrs.)

• Radiometric errors
• Atmosphere-induced errors
• Effects of Atmosphere on radiation
• Errors in imageries (Haze, Sun angle, Atmospheric scattering)
• Correction of atmospheric effects
• Sensor-induced errors
• Effects of sensors
• Errors in imageries (Noise, Spike, Line dropout, Striping)
• Correction of sensor-induced errors

5. Image Geometric Correction (8 hrs.)

• Sources of Geometric errors
• Earth rotation, Earth curvature
• Platform: altitude, attitude, velocity
• Correction of geometric errors
• Geo-referencing, Geocoding process
• Rational polynomial coefficients, Ground control points, stereo imageries
• Distribution of control points, resampling, and rectification
• Image registration, image-to-image registration

6. Image Interpretation (6 hrs.)

• Visual Image interpretation
• Image interpretation elements
• Image Pansharpening
• Pansharpening techniques (Principal Component, HIS, HPF)
• Comparison of different methods
• Selection of Pansharpening technique
• Use of operator and Filters
• Brightness contrast adjustment
• Use of statistical and convolution filters (Majority filter, adaptive, edge detection, edge enhancement, Min max, etc.)
• Histogram adjustment (Histogram Equalization, Linear Stretching, Slicing, etc.)

7. Image Classification (8 hrs.)

• Feature space and clusters
• Unsupervised classification
  • Number of classes and classification parameters
  • Merging and allocation of classes
  • Advantages and limitations
• Supervised classification
  • Supervised classification process
  • Signature collection: methods and parameters
  • Accuracy assessment and error matrix
• Selection of supervised classification
• Object-based classification
• Classification method
• Application of classification

8. Application of Remote Sensing (6 hrs.)

• Mapping and Change analysis
• Forestry and Agriculture
• Civil Engineering and Construction
• Ecosystem and Biodiversity
• Weather and Environment
• Energy production and Management

Exercise:

a. Checking image metadata, RPC data, Spatial, Spectral, and radiometric resolution b. Prepare color composite image, Image radiometric and geometric correction c. Image enhancement, Image interpretation, and feature extraction d. Signature collection, Image classification, accuracy assessment e. Image pansharpening, comparisons of the pansharpening methods

References:

1. Introduction to Remote Sensing, A. P. Cracknel and L. W. B. Hayes, Taylor and Francis
2. Introduction to Remote Sensing, James B. Campbell, Taylor & Francis, 1996
3. Introductory Remote Sensing: Digital Image Processing and Applications, Paul Gibson and Clare Power, Taylor and Francis
4. Introductory Remote Sensing: Principles and Concepts, Paul Gibson and Clare Power, Taylor and Francis
5. Manual of Remote Sensing, Vol. 3, R N Colwell (ed.), American Society of Photogrammetry, Falls Church, Virginia
6. Remote Sensing and Image Interpretation, Thomas M. Lilles and Ralph W. Keifer, John Wiley and Sons, Inc.
7. Remote Sensing Digital Image Analysis: An Introduction, John A. Richards, Springer-Verlag Berlin Heidelberg
8. Lecture Notes

Final Examination Scheme

Model Questions

Very Short Questions: [4*2=8]

1. List the different types of satellites based on orbits.
2. Write the radiometric errors in images.
3. List the image interpretation elements.
4. What are the different fields of application of Remote Sensing Technology?

Short Questions: [8*4=32]

1. Explain the interaction of EM energy with atmosphere and atmospheric window.
2. Describe the working principle of pushbroom scanners.
3. Explain the application of thermal remote sensing.
4. Explain the process of correction of line dropout error in satellite imagery.
5. Describe the geocoding process of high-resolution satellite images by using GCPs.
6. Compare the Principal Component method with the HPF method.
7. Explain the process of signature collection for supervised classification of imageries.
8. How can you select the suitable image for updating topographical base maps?

Long Questions: [2*10=20]

1. Explain the viewer-to-viewer image registration method and its importance.
2. When do you select supervised image classification and compute the accuracy of image classification?
   OR,
   Explain the role of Remote Sensing in agriculture and food security.