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中文摘要：
A supervised polarimetric SAR land cover classification method was proposed based on the Fisher linear discriminant.The feature parameters used in this classification method could be selected flexibly according to land covers to be classified.Polarimetric and texture feature parameters extracted from co-registered multifrequency and multi-temporal polarimetric SAR data could be combined together for classification use,without consideration of the dimension difference of each feature parameter and the joint probability density function of those parameters.Experimental result with AGRSAR L/C-band full polarimetric SAR data showed that a total classification accuracy of 94.33% was achieved by combining the polarimetric with texture feature parameters extracted from L/C dual band SAR data,demonstrating the effectiveness of this method.
英文摘要：
A supervised polarimetric SAR land cover classification method was proposed based on the Fisher linear discriminant. The feature parameters used in this classification method could be se- lected flexibly according to land covers to be classified. Polarimetric and texture feature parameters extracted from co-registered multifrequency and multi-temporal polarimetric SAR data could be com- bined together for classification use, without consideration of the dimension difference of each fea- ture parameter and the joint probability density function of those parameters. Experimental result with AGRSAR L/C-band full polarimetric SAR data showed that a total classification accuracy of 94. 33% was achieved by combining the polarimetric with texture feature parameters extracted from L/C dual band SAR data, demonstrating the effectiveness of this method.
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