MONTE CARLO NON-LOCAL MEANS METHOD FOR HYPERSPECTRAL IMAGE DENOISING

被引：0

作者：

Deng, Chuyin ^{[1
]}

Li, Liyan ^{[1
]}

He, Zhi ^{[1
]}

Li, Jun ^{[1
]}

Zhu, Yuanhui ^{[2
]}

机构：

[1] Sun Yat Sen Univ, Sch Geog & Planning, Ctr Integrated Geog Informat Anal, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China

[2] Guangzhou Univ, Sch Geog Sci, Ctr Geog Informat Anal Publ Secur, Guangzhou 510275, Guangdong, Peoples R China

来源：

IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2018年

基金：

中国国家自然科学基金;

关键词：

Hyperspectral image ( HSI); denoising; classification; Monte Carlo non-local means ( MCNLM);

D O I：

暂无

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：

Hyperspectral image (HSI) denoising has become an important research topic in the research community due to its significance improvements in many applications (e.g. classification). In this paper, we introduce a Monte Carlo non-local means (MCNLM) method for noise reduction of the HSI. Each band of the HSI is processed by the MCNLM, which is a randomized algorithm suitable for large-scale patch-based image (e.g. HSI) filtering. More specifically, the MCNLM is achieved by randomly choosing a fraction of the similarity weights to obtain an approximated result. Compared to the classical non-local means (NLM), the MCNLM consumes less time while achieves comparable performance. Experimental results on the real hyperspectral data set demonstrate the promising performance of the MCNLM for HSI denoising.

引用

页码：4772 / 4775

页数：4

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