- 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
