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

IEEE Access

卷:6

Zhen Yang¹  Lizhen Deng¹  Hu Zhu¹  Yujie Li² 

[1] College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China;

[2] School of Information Engineering, Yangzhou University, Yangzhou, China;

关键词: Hyperspectral image;    sparse noise removal;    low-rank;    tensor;   

DOI  :  10.1109/ACCESS.2018.2876038

来源: DOAJ

【 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.

【 授权许可】

Unknown