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.
Different hexagons configuration aperture models of the optical telescope mirror is carefully considered in this study. The point spread function and the modulation transfer function of a reference star using different hexagons configuration are computed and the quantitative assessment for the results are described. It has been shown that the height of the point spread function decreases rapidly when the area of the circular aperture of the optical telescope is 18 times the area of the individual hexagon to be arrange to fill this aperture. No significant change has been noticed as the area of the this aperture exceeds 121 times the area of the individual hexagon.
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