Optimization at multi-spectral land cover classification with fuzzy clustering and the Kohonen feature map

The number and structure of land cover classes separatable in a region on the basis of multi-spectral satellite images are usually known. The method presented here is capable of combining information from the spectral and non-spectral channels of multi-spectral images in a way that offers a possibility to cover all spectrally separable classes. The method also enables the determination of the representativeness of the available training areas with respect to these classes. In the course of the procedure, 5-35 classes are iteratively determined in succession for different channel combinations with the Kohonen feature map and the fuzzy c-means clustering algorithm. The maximum Jeffries-Matusita (JM) distance between these classes indicates the optimum class number of unambiguously separable classes for the entire multi-spectral image. In a simple step, the available training areas are grouped by the fuzzy c-means clustering algorithm. In this case, the maximum JM distance indicates the class number that ensures optimum separation of training areas. If this class number is smaller than the optimum class number determined for the entire scene, then further training areas must be defined in order to improve the results of a subsequent supervised land cover classification. The method's efficiency is demonstrated by the example of land cover classification for the region around Perpignan.