Data-driven PolSAR Unsupervised Classification Based on Adaptive Model-based Decomposition

摘要：

　　In this paper,we propose a data-driven unsupervised PoiSAR image classification algorithm using adaptive model-based decomposition.By estimating a mean orientation angle and a degree of randomness for canopy scattering,the adaptive decomposition method provides a refined expression of volume scattering component in which no scattering reflection assumption is required.The classification algorithm combines affinity propagation and iterated Wishart classifier.In terms of being data-driven,it can automatically determine the number of clusters.Experimental results on the NASA/JPL AIRSAR L-band PolSAR data of San Francisco demonstrate the effectiveness of our algorithm.

关键词： polarimetric SAR adaptive decomposition model unsupervised classification affinity propagation

作者: Xiaotang Wang Hui Song Wen Yang Xin Xu

作者单位: Department of Physics, Tsinghua University, Beijing 100084, China School of Electronic Information, Wuhan University, Wuhan 430072, China

会议类型: 国际会议

会议名称: 2012 IEEE 11th International Conference on Signal Processing (第11届IEEE信号处理国际会议)

会议地点: 北京

会议语种:英文

页码: 2053-2056

在线出版日期: 2012-10-21（万方平台首次上网日期，不代表论文的发表时间）