A Low-Rank Tensor Model for Hyperspectral Image Sparse Noise Removal

被引：2
|
作者：
Deng, Lizhen [1 ]
Zhu, Hu [1 ]
Li, Yujie [2 ,3 ]
Yang, Zhen [1 ]
机构：
[1] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China
[2] Yangzhou Univ, Sch Informat Engn, Yangzhou 225127, Jiangsu, Peoples R China
[3] Fukuoka Univ, Fac Engn, Fukuoka, Fukuoka 8140180, Japan
来源：
IEEE ACCESS | 2018年 / 6卷
基金：
中国博士后科学基金;
关键词：
Hyperspectral image; sparse noise removal; low-rank; tensor; DECOMPOSITION; RECOVERY;
D O I：
10.1109/ACCESS.2018.2876038
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Hyperspectral image (HSI) has been widely used in target detection and classification. However, various kinds of noise in HSIs affect the applications of HSIs. In this paper, we propose a low-rank (LR) tensor recovery model to remove noise. Considering that the HSI is a 3-D HSI data, and the underlying LR tensor property is used in the model. Then, according to the similarity of adjacent bands images, the regularization on the difference of adjacent bands images is considered. The experiments of removing noise from different noisy HSIs show that our method can achieve better performance on removing sparse noise, especially for strips removal.
引用
收藏
页码：62120 / 62127
页数：8
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