Hyperspectral Imagery Denoising Using Minimum Noise Fraction and Video Non-Local Bayes Algorithms

被引：3

作者：

Chen, Guang Yi ^{[1
]}

Krzyzak, Adam ^{[1
]}

Qian, Shen-En ^{[2
]}

机构：

[1] Concordia Univ, Dept Comp Sci & Software Engn, Montreal, PQ H3G 1M8, Canada

[2] Canadian Space Agcy, Space Sci & Technol, St Hubert, PQ J3Y 8Y9, Canada

来源：

CANADIAN JOURNAL OF REMOTE SENSING | 2022年 / 48卷 / 05期

关键词：

D O I：

10.1080/07038992.2022.2116307

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

Hyperspectral imagery (HSI) denoising is a popular research topic in remote sensing. In this paper, we propose a novel method for HSI denoising by performing Minimum Noise Fraction (MNF) to the original HSI data cube, thresholding the noisy output bands with the Video Non-Local Bayes (VNLB) algorithm, and then conducting the inverse MNF transform to obtain the denoised data cube. Our experiments demonstrate that the proposed method usually achieves the best denoising results among several existing denoising methods for two HSI data cubes. In addition, it is much faster for HSI denoising than the VNLB algorithm which was originally developed for video denoising.

引用

页码：694 / 701

页数：8

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