BITEMPORAL FULLY POLARIMETRIC SAR IMAGES CHANGE DETECTION VIA NEAREST REGULARIZED JOINT SPARSE AND TRANSFER DICTIONARY LEARNING

被引：0

作者：

Tan, Yao ^{[1
]}

Li, Jichao ^{[1
]}

Zhang, Peiyang ^{[1
]}

Gou, Shuiping ^{[1
]}

Wang, Peng ^{[1
]}

Chen, Yuanbo ^{[2
]}

Chen, Jia-Wei ^{[1
]}

Sun, Changyan ^{[2
]}

机构：

[1] Xidian Univ, Sch Artificial Intelligence, Xian 710071, Peoples R China

[2] Beijing Huahang Radio Measurement & Res Inst, Beijing 100013, Peoples R China

来源：

2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年

基金：

中国国家自然科学基金;

关键词：

Polarimetric SAR; Joint sparse representation; Change detection; REPRESENTATION;

D O I：

10.1109/igarss.2019.8897906

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Most current synthetic aperture radar (SAR) images change detection methods are developed based on a difference image. In this paper, we propose a novel bitemporal polarmetric SAR (PolSAR) images change detection framework based on their land-covers classifications. First, a nearest regularized joint sparse representation (NRJSR) model is developed to exploit the correlations among various polarimetric information and spatial context. Next, a transfer dictionary learning method is proposed for bitemporal PolSAR images classifications. Finally, the changed map can be obtained by comparing these two classification results. The comparison experiment results show that the proposed algorithm obtains better performance.

引用

页码：21 / 24

页数：4

共 21 条

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