期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B7
页码：21-26
出版社：Copernicus Publications
摘要：Polarimetric SAR can observe scattering matrix for each resolution cell and provide amplitude images that have gray level in proportion to amplitude of the conjunction matrix elements. However, these amplitude image data have been used for pseudo color synthesize and construction of feature vector for land-cover classification as the vector elements mainly, discussion about features derived from amplitude image data was scarcely. In this paper, we consider pattern difference in different polarization amplitude image of polarimetric SAR as probability, and discuss about contribution of expanding dimension of feature vector by introducing Rajski distance as a features. To calculate Rajski distance, gray level co-occurrence matrix (GLCM) method that has been often used for texture analysis was used. In the proposed method, gray levels of the pixel that was located at the same coordinates in two different transmit and receive polarization amplitude images are adapted to line and column of GLCM, then co-occurrence probability are calculated. From this matrix, joint entropy and conditional entropy are derived and Rajski distance is found. To inspect the proposed theory, we investigate the effect of expanding dimension of feature vector for land-cover classification by Euclid minimum distance method and maximum likelihood method as well known supervised classification method generally using SIR-C polarimetric data obtained in two kinds of scenes including different land-cover objects. As the results, improvement of accuracy in the point of classification score and ambiguity scale, so that the effectiveness of introduction of Rajski distance to expanding dimension of feature vector for land-cover classification is demonstrated
关键词：Remote Sensing; Land Cover; Classification; Polarization; Algorithms; SAR
