Hyperspectral image denoising via sparsity and low rank

Yongqiang Zhao, Jinxiang Yang

科研成果: 书/报告/会议事项章节会议稿件同行评审

17 引用 (Scopus)

摘要

Hyperspectral noise is unavoidable in capture and transmission process, and it will degrade the detection and classification performance greatly. Noise free signal can be approximated using few atom or basis, while noisy signal is not. There are lots of similar spatial-spectral structures in noise free hyperspectral image. On the other hand, hyperspectral image of different bands are highly correlated, the rank of hyperspectral data should be low. Based on these ideas, in this paper, we propose a hyperspectral denoising method in sparse representation framework with low rank and nonlocal regulation. Numerical experiment demonstrates that proposed denoising result is competitive with the state of art algorithm.

源语言英语
主期刊名2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings
1091-1094
页数4
DOI
出版状态已出版 - 2013
活动2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Melbourne, VIC, 澳大利亚
期限: 21 7月 201326 7月 2013

出版系列

姓名International Geoscience and Remote Sensing Symposium (IGARSS)

会议

会议2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013
国家/地区澳大利亚
Melbourne, VIC
时期21/07/1326/07/13

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