Image segmentation and discriminant analysis for the identification of land cover units in ecology

The textured nature of most natural land cover units as represented in remotely sensed imagery causes limited results of per-pixel classifications. The segmentation algorithm, iterative mutually optimum region merging (IMORM), is presented and used to partition images into elements that are thereafter classified by linear canonical discriminant analysis and a maximum likelihood allocation rule. This per-segment approach results in much higher accuracy than the conventional per-pixel approach. Furthermore, separability matrices indicate that many land cover categories cannot be correctly defined by per-pixel statistics.