张量组稀疏表示的高光谱图像去噪算法

王忠美; 杨晓梅; 顾行发

doi:10.11947/j.AGCS.2017.20150403

测绘学报 >

2017 , Vol. 46 >Issue 5: 614 - 622

DOI: https://doi.org/10.11947/j.AGCS.2017.20150403

摄影测量学与遥感

张量组稀疏表示的高光谱图像去噪算法

王忠美 ,
杨晓梅 ,
顾行发

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1. 电子科技大学四川成都 610054;
2. 中国科学院地理科学与资源研究所, 北京 100101;
3. 中国科学院遥感与数字地球研究所, 北京 100101

王忠美(1984-),男,博士生,研究方向为遥感影像处理。E-mail:ldwangzm2008@163.com

收稿日期: 2015-08-21

修回日期: 2017-03-05

网络出版日期: 2017-06-05

基金资助

国家重点研发计划（2016YFB0501404；2016YFC1402003）；国家自然科学基金（41671436）

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Hyperspectral Image Denoising Based on Tensor Group Sparse Representation

WANG Zhongmei ,
YANG Xiaomei ,
GU Xingfa

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1. University of Electronic Science and Technology of China, Chengdu 610054, China;
2. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
3. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Received date: 2015-08-21

Revised date: 2017-03-05

Online published: 2017-06-05

Supported by

The National Key Research and Development Program of China(Nos.2016YFB0501404;2016YFC1402003);The National Science Foundation of China under Grant (No.41671436)

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摘要

提出了一种基于张量组稀疏表示的高光谱遥感影像降噪。高光谱影像数据可视为三阶张量。首先，高光谱图像被划分为小的张量分块，然后，对相似的张量分块进行聚类，并对聚类分组进行稀疏表示。基于高光谱图像的空间非局部自相似性和光谱相关性，将张量组稀疏表示模型分解为一系列无约束低秩张量的近似问题，进而通过张量分解进行求解。对模拟和真实高光谱数据进行试验，验证了该算法的有效性。

关键词： 高光谱图像; 张量; 稀疏表示; 非局部相似性

本文引用格式

王忠美 , 杨晓梅 , 顾行发 . 张量组稀疏表示的高光谱图像去噪算法[J]. 测绘学报, 2017 , 46(5) : 614 -622 . DOI: 10.11947/j.AGCS.2017.20150403

Abstract

A novel algorithm for hyperspectral image (HSI) denoising is proposed based on tensor group sparse representation. A HSI is considering as 3 order tensor. First, a HSI is divided into small tensor blocks. Second, similar blocks are gathered into clusters, and then a tensor group sparse representation model is constructed based on every cluster. Through exploiting HSI spectral correlation and nonlocal similarity over space, the model constrained tensor group sparse representation can be decomposed into a series of unconstrained low-rank tensor approximation problems, which can be solved using the tensor decomposition technique. The experiment results on the synthetic and real hyperspectral remote sensing images demonstrate the effectiveness of the proposed approach.

Key words： hyperspectral image; tensor; sparse representation; nonlocal similarity

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