DYNAMIC DICTIONARY LEARNING STRATEGIES FOR SPARSE REPRESENTATION BASED HYPERSPECTRAL IMAGE ENHANCEMENT

被引：0

作者：

Grohnfeldt, Claas ^{[1
]}

Burns, Tristan Michael ^{[2
]}

Zhu, Xiao Xiang ^{[1
,3
]}

机构：

[1] DLR German Aerosp Ctr, Remote Sensing Technol Inst, Munchener Str 22, D-82234 Wessling, Germany

[2] Univ Queensland, Sch Mech & Min Engn, Brisbane, Qld, Australia

[3] TUM, Chair Remote Sensing Technol, Munich, Germany

来源：

2015 7TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS) | 2015年

关键词：

dictionary learning; image fusion; hyperspectral resolution enhancement; sparse representation; FUSION;

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：

This paper introduces four new dynamic dictionary learning methods to sparse representation based hyperspectral resolution enhancement. The impact of the type and size of the dynamic dictionary on the reconstruction quality is investigated for the recently proposed sparse representation based multiresolution image fusion method J-SparseFI-HM. Low resolution hyperspectral and high resolution multispectral input images are simulated from recently acquired airborne HySpex data. Experiments reveal that fusion products can be substantially improved by changing the dictionary type from the currently used nearest neighbor selection to a modified dissimilarity based dynamic dictionary.

引用

页数：4

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