Course Objectives:
The course objective is to introduce the modern quantitative approach of survey technique. It uses remote sensing techniques in optical, infrared and microwave radiation zones. At the end of the course, the students gain theoretical knowledge and practical approach on information extraction by the use of electromagnetic radiation.

Course Content:

  1. Introduction (3 hours)
    1. What is remote sensing? Why remote sensing? Types of remote sensing
    2. Historical perspective, photo interpretation, quantitative analysis, evolution of ecosystem, spaced based ecosystem
    3. Digital concepts

  2. Radiation principles (6 hours)
    1. Electromagnetic radiation, Electromagnetic Spectrum, EM properties, classification of EMS,
    2. Physical principles of radiation in remote sensing, energy conservation principle, radiometric terminology, use of physical principles in remote sensing

  3. Remote Sensing Systems (6 hours)
    1. Spatial and radiometric characteristics, spectral characteristics, Temporal characteristics
    2. Camera and Film System, Imaging sensor types, instrument response, spatial response, spectral response, PSF (optical, sensor, image motion, electronic)
    3. Imaging system simulation, PSF measurement, spectral response, Signal amplification, sampling and quantization
    4. Active remote sensing system: Types, microwave remote sensing, active microwave, geometry od radar image, wavelength polarization, look angle and direction, RAR, SAR, radar interferometry

  4. Image Statistics (6 hours)
    1. Uni-variate statistics (histogram, normal distribution, cumulative histogram), Multivariate statistics, noise models, statistical measure of image quality (Modulation, SNR, constrast)
    2. Spatial statistics (covariance and semi-varlogram), power spectral density, Co- occurrence matrix, fractal geometry, topographic and sensor effects (topography and spectral scattergrams, Sensor Characteristcs and Spatial Statistics, Sensor  Characteristics and Spectral Scattergrams

  5. Radiometric distortion (6 hours)
    1. Sources of Radiometric Distortion, The Effects of the Atmosphere on Radiation, Atmospheric Effects on Remote Sensing Imagery
    2. Instrumentation Errors, Correction of Radiometric Distortion, Detailed Correction of Atmospheric Effects, Bulk Correction of Atmospheric Effects

  6. Geometric distortions (6 hours)
    1. Sources of Geometric Distortion, Earth Rotation Effects, Panoramic Distortion,     Earth Curvature, Scan Time Skew, Variations in Platform Altitude, Velocity and Attitude, Aspect Ratio Distortion, Sensor Scan Nonlinearities,
    2. Correction of Geometric Distortion, Use of Mapping Polynomials for Image Correction mapping Polynomials and Ground Control Points, Resampling, Interpolatoin, Choice of control points, mathematical Modelling-Aspects Ratio Correction, Earth Rotation Skew Correction, Image Orientation to North-South, Correction of Panoramic Effects, Combining the Corrections
    3. Image Registration, Georeferencing and Geocoding, Image to Image Registra-tion rectification

  7. The Interpretation of Digital Image (6 hours)
    1. Approaches to Interpretation, Forms of imageries for Photo interpretation, Computer Processing for Photo interpretation, Multispectral Space and spectral Classes
    2. Pattern Recognition, Pixel Vectors and Labelling, Unsupervised Classification (Delineation of Spectral Classes, Similarity Metrics and Clustering Criteria, The Iterative Optimization (Migrating Means) Clustering, Algorithm, The Basic Algorithm, Merging and Delections, Splitting Elongated Cluster, Choice of Initial Cluster Centre, Unsupervised Classification and Cluster Maps, Clustering Example, Single Pass Clustering Technique, Advantages and Limitations, Agglomerative Hierarchical Clustering, Clustering by Histogram Peak Selection
    3. Supervised Classification (Steps in Supervised Classification, Maximum Likelihood Classification, Bayes' Classification, The Maximum Likelihood Decision Rule, Pixels, Required for Each Class, Minimum Distance Classification, The Discriminant, Degeneration of Maximum Likelihood to Minimum Distance Classification, Decision Surfaces, Thresholds, Parallelepiped Classification)

  8. Map accuracy (3 hours)
    1. The History of Map Accuracy Assessment, Positional Accuracy Thematic   Accuracy Non-Site-Specific Assessments Site-Specific Assessment, The Error Matrix Mathematical Representation of the Error Matrix,
    2. Sample Design Considerations, Appropriate Sample Unit, Reference Data Collection, Analysis Techniques

Tutorial and Practical: (37.5 hours)

  1. Electromagnetic energy exercise-sensor design, spectral reflectance curve
  2. Working with images-opening images, making colour combination, location value extraction,
  3. Atmospheric correction-using dark object substractoin, empirical line and refined empirical line method
  4. Geometric correction-GCP points, maps to image, image to image registration
  5. Unsupervised classification-ISO data and K-means
  6. Supervised classification-parallelepiped, nearest neighbourhood, maximum likelihood
  7. Accuracy assessment of supervised and unsupervised classification
  8. Image operations-sub-setting in spatial and spectral domain, layer stacking


  1. Remote Sensing and GIS by B Bhatta, Oxford University Press
  2. Digital Image Processing by Rafael C. Gonzalec and Richard E. Woods, Pearson Education
  3. Remote Sensing and Image Interpretation by Thomas M.Lillesand, Ralf W. Klefer and Jonnathan W.Chipman, Wiley India
  4. Schowengerdt R.A., Remote Sensing: Models and Methods for image processing, Elsevier, 2007
  5. George Joseph, Fundamentals of Remote Sensing, Universities Press, 2005
  6. B. Campbell, Introduction to Remote Sensing Taylor & Francis, 2002
  7. John A. Richards, Xiuping Jia, Remote Sensing Digital Image Analysis: An Introduction Springer, 2006
  8. Different publications

Evaluation Scheme:
The questions will cover all the chapters in the syllabus. The evaluation scheme will be as indicated in the table below:




Marks allocation*










4.2, 5














*Note: There may be minor deviation in marks distribution

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