Supervised polarimetric SAR classification method based on Fisher linear discriminant

• 作 者: WANG Peng LI Yang HONG Wen
• 机 构: National Key Laboratory of Microwave Imaging Science and Technology, Graduate University ofChinese A
• 出 处: 北京理工大学学报（英文版）
• 发表日期: 2004年期
• 关键词: polarimetric SAR land cover classification supervised classification Fisher line
• 基金项目: ESA-MOST Dragon 2 Cooperation Programme;the National High-Tech R&D Program (\"863\" Program);国家自然科学基金

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 94.339％ was achieved by combining the polarimetric with texture feature parameters extracted from L/C dual band SAR data,demonstrating the effectiveness of this method.