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  • Fundamentals of an RS image: format, pixels, DN, image formats- BIP, BIL, BSQ, data correction
  • Reading the data: Image statistics, histograms, ratios
  • Pre-processing the data: correcting and restoring- geometric and radiometric, atmospheric correction- noise removal
  • Stitching together RS images: mosaicking
  • Manipulating Image contents: Spectral Ratio- vegetation, soil, and other indices
  • Classifying RS Images: basics, unsupervised classification-K-means, ISODATA; supervised classification-stages, algorithms-Minimum distance to mean, Parallelepiped, maximum likelihood classifiers. Comparing classification methods.
  • Resampling: Interpolation methods- nearest neighborhood (NNI), bilinear interpolation (BII), and cubic convolution (CCI).
  • Post classification tasks: recoding, filtering, smoothening, assessing accuracy, area statistics.
  • Analyzing and comparing classified Images: Change Detection