WAVELET-BASED BLOCK LOW-RANK REPRESENTATIONS FOR HYPERSPECTRAL DENOISING

被引：2

作者：

Zhao, Bin ^{[1
]}

Sveinsson, Johannes R. ^{[1
]}

Ulfarsson, Magnus O. ^{[1
]}

Chanussot, Jocelyn ^{[1
,2
]}

机构：

[1] Univ Iceland, Fac Elect & Comp Engn, Reykjavik, Iceland

[2] Univ Grenoble Alpes, GIPSA Lab, Grenoble INP, CNRS, F-38000 Grenoble, France

来源：

2021 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM IGARSS | 2021年

关键词：

Denoising; hyperspectral image; wavelet; block low-rank representations; IMAGE; REDUCTION; SPARSE;

D O I：

10.1109/IGARSS47720.2021.9554582

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

This paper presents a wavelet-based block low-rank representations (WBBLRR) denoising method for hyperspectral images (HSIs). WBBLRR uses 3-D wavelet transformation to decompose HSI into different blocks, where each block utilizes a low-rank representations model to obtain the denoised block, and then uses inverse 3-D wavelet transformation for all the denoised blocks to obtain the denoised HSI. The proposed method is evaluated by using both simulated and real hyperspectral datasets.

引用

页码：2484 / 2487

页数：4

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