HYPERSPECTRAL STRIPES REMOVAL WITH WAVELET-DOMAIN LOW-RANK/GROUP-SPARSE DECOMPOSITION

被引：1
|
作者：
Liu, Na [1 ]
Li, Wei [2 ]
Tao, Ran [2 ]
Fowler, James E. [3 ]
Yang, Lina [4 ]
机构：
[1] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing, Peoples R China
[2] Beijing Inst Technol, Sch Informat & Elect, Beijing, Peoples R China
[3] Mississippi State Univ, Dept Elect & Comp Engn, Mississippi State, MS 39762 USA
[4] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China
来源：
2019 10TH WORKSHOP ON HYPERSPECTRAL IMAGING AND SIGNAL PROCESSING - EVOLUTION IN REMOTE SENSING (WHISPERS) | 2019年
基金：
北京市自然科学基金; 中国国家自然科学基金;
关键词：
Destriping; hyperspectral imagery; low-rank decomposition; group sparsity; wavelet transform;
D O I：
10.1109/whispers.2019.8921401
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Pushbroom acquisition of hyperspectral imagery is prone to striping artifacts in the along-track direction. A hyperspectral destriping algorithm is proposed such that subbands of a 2D wavelet transform most effected by pushbroom stripesnamely, those with spatially vertical orientation-are the exclusive focus of destriping. The proposed method features an iterative image decomposition composed of a low-rank model for the stripes coupled with a group-sparse prior on the wavelet coefficients of the subbands in question. Experimental results on both synthetically striped imagery demonstrate superior image quality for the proposed method as compared to other state-of-the-art methods.
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收藏
页数：4
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