Derivation of land cover information by fuzzy clustering of remotely sensed imagery

Automatic pattern recognition by means of fuzzy logic (fuzzy clustering) had been applied to several fields during the last years. The spectral properties of different land cover types as seen in multiband images can also be interpreted as patterns in the dimension of gray values. Fuzzy clustering therefore is a new promising approach to mapping land cover from remotely sensed images. The traditional method of classifying a remotely sensed image is the transformation via a classification algorithm into a single classified image of the land surface, but natural landscapes present a continuum of variety at many different scales and a high proportion of the discretely sampled pixels within an image contains mixed spectral signatures and are not easily placed into fixed thematic classes. The estimation of fuzzy memberships to vague classes of land cover more faithfully represents the true situation. Within this article the application of the unsupervised fuzzy c means (FcM) algorithm to land cover classification, the suitability and interpretability of the results will be discussed.