PAN-SHARPENING BASED ON NONPARAMETRIC BAYESIAN ADAPTIVE DICTIONARY LEARNING

被引：0

作者：

Xie, Jin ^{[1
]}

Huang, Yue ^{[1
,2
]}

Paisley, John ^{[3
]}

Ding, Xinghao ^{[1
,2
]}

Zhang, Xiao-ping ^{[1
,2
,4
]}

机构：

[1] Xiamen Univ, Dept Commun Engn, Xiamen, Peoples R China

[2] Minist Educ, Key Lab Underwater Acoust Commun & Marine Informa, Xiamen, Peoples R China

[3] Univ Calif Berkeley, Dept EECS, Berkeley, CA 94720 USA

[4] Ryerson Univ, Dept Elect & Comp Engn, Toronto, ON, Canada

来源：

2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013) | 2013年

关键词：

pan-sharpening; dictionary learning; compressed sensing; image fusion; remote sensing; SPECTRAL RESOLUTION IMAGES; FUSION;

D O I：

暂无

中图分类号：

TB8 [摄影技术];

学科分类号：

0804 ;

摘要：

Pan-sharpening based on compressed sensing (CS) theory has been widely studied in recent years. In this paper, we present a novel CS-based pan-sharpening method based on nonparametric Bayesian adaptive dictionary learning. In contrast to existing optimization methods, the proposed method adaptively infers parameters such as dictionary size, patch sparsity and noise variances. In addition, high resolution multiband images, which are unavailable in practice, are not required to learn the dictionary anymore. An IKONOS satellite image is employed to validate the method. Both visual results and quality metrics demonstrate that proposed method is able to achieve higher spatial and spectral resolution simultaneously, compared with other well-known methods.

引用

收藏

页码：2039 / 2042

页数：4

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