Exploiting Spectral, Spatial and Semantic Constraints in the Segmentation of Landsat Images

A critique of traditional classification techniques for LANDSAT images and consideration of some scene analysis techniques, exploiting spatial organization and meaning, lead to a new approach to computer programs for LANDSAT image understanding. To justify this approach, a program that combines modified maximum likelihood techniques with interpretation-controlled region merging methods to interpret forest cover in LANDSAT images is described. For comparison purposes, a pure supervised classifier using the same data made 43% more errors and produced a segmentation twice as complex.