OPTIMAL PROJECTION FROM N-DIMENSIONAL PATTERN SPACE INTO A PLANE

This paper presents a discriminant algorithm that seeks to separate different classes as much as possible for discriminant analysis or dimension reduction. The optimization is achieved through the maximization of the Fisher ratio (which is defined as the ratio of the between-class scatter to the sum of within-class scatters). This algorithm for feature extraction shows improvement over the conventional feature selection algorithms used in remote sensing as well with other applications. The conducted experiments are accomplished using both simulated Gaussian and real airborne MSS/TM satellite data for both large and small sample size. Although the conducted experiments are performed over the case of two classes, extension to n-dimensions can be easily obtained using the binary decision tree.