Post Classification Operations: Smoothing, Evaluating Accuracy of Classification, and Determining Classification Errors (Especially for GATE-Geospatial 2022)

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Problems with Classified Data: Need for Smoothing

Classified data often manifest a salt-and-pepper appearance due to the inherent spectral variability encountered by a classification when applied on a pixel-by-pixel basis. It is often desirable to β€œsmooth” the classified output to show only the dominant (presumably correct) classification.

Smoothing Using Majority Filter

  • One means of classification smoothing involves the application of a majority filter. In such operations a moving window is pass through the classified pixel in the window is not the majority class, its identity is changed to the majority class. If there is no majority class in the window, the identity of the centre pixel is not changed. As the windows progress through the data set, the original class code is continually used, not the labels as modified from the previous window position. (Eastman, 1995) .
  • Majority filters can also incorporate some form of class and spatial weighting function. Data may also be smoothed more than once. Certain algorithms can preserve the boundaries between land cover regions and involve a user-specified minimum area for any given land cover type that will be maintained in the smooth output (Lillesand and Kiefer, 1994) .
Smoothing Zone Edges with Boundary Clean and Majority Filter …

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