Course title

Course code


Machine Processing of Remotely Sensed Images


Introduction:Digital image Processing (DIP) system; components and functions, basic imaging process, multi-concept in RS data analysis, elements of human and computer assisted interpretation

Formats of digital imagery (bmp, jpg, tiff, NRSA formats, Other SW specific formats, colour look up tables and transformations.

Pre-processing of remotely sensed images:Geometric distortions: sources of image geometry errors, altitude, attitude, scan skew, velocity, Earth rotation, map projection, sensor mirror sweep, panoramic, and perspective effects, correction of geometric distortions; model based correction, ground control points, mapping polynomials, image rectification, geo-referencing, registration, resampling, and intensity interpolation.

Radiometric distortions: Sources of radiometric distortion, effect of atmospheric condition on radiation, atmospheric effects on remote sensing imagery, correction of radiometric distortions.

Image enhancement: Image histogram, point operations and look-up tables, contrast enhancements, histogram equalization, spatial and frequency filtering, linear and non-linear filters, smoothing, sharpening

low pass filters, high pass filters: edge detection and enhancement, edge detection operators(Conventional filters); first derivative (Robert, Frei-Chen, Sobel), second derivative (Laplacian, Laplacian of Gaussian (LoG), Difference of Gaussian (DoG), edge thinning and linking, colour edge detection, morphological filters, properties and filters for radar images.

Image transformationsPrincipal component analysis (standardized/unstandardized), Independent Component Analysis, Tasseled cap transformation, band ratios and vegetation indices, Image fusion; Conventional approach, wavelet based approach, Price algorithm.

Pattern recognition: Pattern, image classification, decision surfaces, feature selection, unsupervised classification; k-means clustering, ISODATA, supervised classification; maximum likelihood, parallelepiped, and minimum distance to means, K-NN, training areas and and their characteristics, refinement of training data, Feature selection; divergence analysis, Bhattacharya and Mahalanobis distance, JM distance, separability analysis, classification accuracy estimation, naïve measure, Kappa, Tau indices, fuzzy classification and accuracy analysis, spatial classification; texture, contextual, object-based classification

other classifiers; ANN, SVM classification, binary and hybrid classification, hyperspectral classification.



  1. Mather P.M. and Koch M., Computer Processing of Remotely-Sensed Images: An Introduction, 4th ed., Wiley-Blackwell, Chichester, UK, 2011.
  2. Richards J.A., Remote Sensing Digital Image Analysis, 5th ed., Springer, Berlin Heidelberg, 2013.
  3. Duda R.O., Hart P.E. and Stork D.G., Pattern Classification, 2nd ed., John Wiley, N.Y., 2001.
  4. Jensen J.R., Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd ed., Prentice-Hall, Upper Saddle River, N.J., 2004.
  5. Gonzalez R.C. and Woods R.E., Digital Image Processing, 3rd ed.,Pearson Prentice-Hall, Upper Saddle River, N.J., 2008.
  6. Congalton R.G. and Green K., Assessing the Accuracy of Remotely  Sensed Data: Principles and Practices, 2nd ed., CRC Press Taylor and Francis Group, Boca Raton FL, 2009.
  7. Pratt W.K., Digital Image Processing: PIKS Inside, 3rd ed., John Wiley &Sons, Inc., New York, NY, USA, 2001.
  8. Schowengerdt R.A., Remote Sensing: Models and Methods for Image Processing, 3rd ed., Academic, Burlington, Mass., 2006.