Object-Based Classification Method for PolSAR Images with Improved Scattering Powers and Contextual Features  

作　　者：YUAN Zhengwu CHEN Ran CHEN Cuiping LUO Xiaobo LIU Minghao 

机构地区：[1]College of computer science and technology, Chongqing University of Posts and Telecommunications

出　　处：《Chinese Journal of Electronics》2017年第4期803-809,共7页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.41301384);Scientific and Technological Research Program of Chongqing Municipal Education Commission(No.KJ120517,No.KJ1400420)

摘　　要：This paper proposes a new object-based classification method for Polarimetric synthetic aperture radar(Pol SAR) images, which considers scattering powers from an improved model-based polarimetric decomposition approach, as well as the spatial and textural features. With the decomposition, the scattering ambiguities between oriented buildings and vegetation are reduced. Furthermore,various contextual features are extracted from the object and incorporated into the K-nearest neighbors(k-NN)based classification. To reduce the feature redundancy, a new Supervised locally linear embedding(S-LLE) dimensionality reduction method is introduced to map the high dimensional polarimetric signatures into the most compact low-dimensional structure for classification. Experimental results with Airborne synthetic aperture rada(AIRSAR)C-band Pol SAR image demonstrate the superior performance to other methods.This paper proposes a new object-based classification method for Polarimetric synthetic aperture radar（Pol SAR） images, which considers scattering powers from an improved model-based polarimetric decomposition approach, as well as the spatial and textural features. With the decomposition, the scattering ambiguities between oriented buildings and vegetation are reduced. Furthermore,various contextual features are extracted from the object and incorporated into the K-nearest neighbors（k-NN）based classification. To reduce the feature redundancy, a new Supervised locally linear embedding（S-LLE） dimensionality reduction method is introduced to map the high dimensional polarimetric signatures into the most compact low-dimensional structure for classification. Experimental results with Airborne synthetic aperture rada（AIRSAR）C-band Pol SAR image demonstrate the superior performance to other methods.

关 键 词：Object-based Polarimetric decomposi tion Contextual features Supervised locally linear embed ding(S-LLE) Polarimetric synthetic aperture radar(PolSAR) 

分 类 号：TN957.52[电子电信—信号与信息处理]