多视极化合成孔径雷达图象的分类和极化通道优化
CLASSIFICATION OF MULTI-LOOK POLARIMETRIC SAR IMAGERY AND POLARIZATION CHANNEL OPTIMIZATION
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摘要: 本文提出一个新的最大似然(ML)分类算法对多视全极化合成孔径雷达(SAR)图象进行分类,给出了应用NASA/JPL机载L波段四视全极化SAR实测数据的试验结果,证明了新算法的有效性。此外,本文还将所提算法应用于部分的多视全极化SAR数据中,实现了地貌类型分类的极化通道优化。Abstract: In this paper, a new maximum likelihood (ML) classification algorithm is proposed to classify the multi-look polarimetric synthetic aperture radar (SAR) imagery. Experimental results with the NASA/JPL airborne L-band 4-look polarimetric SAR data demonstrate the effectiveness of the new algorithm. Furthermore, when using the algorithm in the classifications with subsets of the multi-look polarimetric SAR data, the polarization-channel optimization for the terrain type classification is implemented.
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Lim H, Swartz A A, et al. Classification of earth terrain using polarimetric SAR images[J].J. of Geophysical Research.1989, 94(136):7049-7057[2]De Grandi G, Lemoine G, Sieber A. Supervised Fully Polarimetric Classification: An Experimental Study on the Maestro-1 Freiburg Data Set. Proc. IGARSS92, Huston, TX, USA: 1992, 782-785.[3]Kong J A, Swartz A A, et al. Identification of terrain cover using the optimum polarimetric classifier. J. of Electromag. Waves and Appl., 1988, 2(2): 171-194.[4]Lee J S, Gruns M R. Classification of multi-look polarimetric SAR imagery based on complex Wishart[5]distribution. Int[J].J. Remote Sensing.1994, 15(11):2299-2311[6]Frost V S, Yurovsky L S. Maximum likelihood classification of synthetic aperture radar imagery[J].Computer Vision, Graphic and Image Processing.1985, 32(2):291-313[7]Lin Q, Allebach J P. Combating speckle in SAR imagery: Vector filtering and sequential classification based on a multiplicative noise model. IEEE Trans. on GE, 1990, GE-28(4): 674-653.[8]Ulaby F T, Elachi C (Ed.). Radar Polarimetry for Geoscience Application, MA: Artech House INC, 1990, Ch. 2.[9]刘国庆.极化合成孔径雷达成像的理论分析及应用研究:[博士论文].成都:电子科技大学,1996. -
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