Identification of Band Reflectance for Each Category and Land-Cover Classification Using Fuzzy Least-Squares Method

This paper deals with application of fuzzy least square method to the land-cover classification of remotely sensed data. The proposed method has a considerable advantage of extracting the reflectance of spectral bands for each category from pixel data and thus avoiding difficulties in electing training sets for each land-cover class when the class number gets large, which exist in conventional supervised classification. From the test results it was verified that the proposed method can classify land-cover in minute classes and make it possible to carry out some special land-cover classification. The accuracy of classification turned out to be improved, compared with the three existing representative classification techniques of existing methods, maximum likelihood method, linear discriminant function method and quadratic programming method, and the proposed method can be certainly regarded as a practical tool for land-cover classification of remotely sensed data.