Pansharpening Based on Low-Rank and Sparse Decomposition

被引：32

作者：

Rong, Kaixuan ^{[1
]}

Jiao, Licheng ^{[1
]}

Wang, Shuang ^{[1
]}

Liu, Fang ^{[1
,2
]}

机构：

[1] Xidian Univ, Minist Educ China, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China

[2] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China

来源：

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING | 2014年 / 7卷 / 12期

基金：

中国国家自然科学基金; 新加坡国家研究基金会;

关键词：

Component substitution (CS); context-based decision (CBD) model; Go Decomposition (GoDec); low-rank and sparse (LRS) decomposition; multispectral (MS) images; panchromatic (PAN) image; SPECTRAL RESOLUTION IMAGES; ARSIS CONCEPT; FUSION; MULTIRESOLUTION; QUALITY; MATRIX;

D O I：

10.1109/JSTARS.2014.2347072

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：

This paper explores the low-rank and sparse (LRS) decomposition to solve the problem of pansharpening. By exploiting the significant correlation among the multispectral (MS) image bands, the LRS decomposition is employed as a decorrelation tool, from which the spectral and spatial informations in MS images can be separated. Based on Go Decomposition (GoDec), we provide two contributions. 1) An LRS-based pansharpening method (i.e., ImPCA) which is designed in terms of component substitution (CS) concept is given. 2) In order to improve the performance of ImPCA by reducing the spectral distortion which is characterized by the color or radiometric changes in the pansharpened images, the local dissimilarity between MS and panchromatic (PAN) images is taken into account by exploiting the context-based decision (CBD) model. Experimental results with both simulated and real data demonstrate that after the local dissimilarity is considered, the quality of the pansharpened images is significantly improved. The improved version of ImPCA is comparable with other popular methods.

引用

页码：4793 / 4805

页数：13

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