Abstract
A hyperspectral image compression method is proposed using an online dictionary learning approach. The online learning mechanism is aimed at utilizing least number of dictionary elements for each hyperspectral image under consideration. In order to meet this "sparsity constraint", basis pursuit algorithm is used. Hyperspectral imagery from AVIRIS datasets are used for testing purposes. Effects of non-zero dictionary elements on the compression performance are analyzed. Results indicate that, the proposed online dictionary learning algorithm may be utilized for higher data rates, as it performs better in terms of PSNR values, as compared with the state-of-the-art predictive lossy compression schemes.
Original language | English |
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Title of host publication | Satellite Data Compression, Communications, and Processing XI |
Editors | Yunsong Li, Chein-I Chang, Bormin Huang, Qian Du, Chulhee Lee |
Publisher | SPIE |
ISBN (Electronic) | 9781628416176 |
DOIs | |
Publication status | Published - 2015 |
Externally published | Yes |
Event | Satellite Data Compression, Communications, and Processing XI - Baltimore, United States Duration: 23 Apr 2015 → 24 Apr 2015 |
Publication series
Name | Proceedings of SPIE - The International Society for Optical Engineering |
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Volume | 9501 |
ISSN (Print) | 0277-786X |
ISSN (Electronic) | 1996-756X |
Conference
Conference | Satellite Data Compression, Communications, and Processing XI |
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Country/Territory | United States |
City | Baltimore |
Period | 23/04/15 → 24/04/15 |
Bibliographical note
Publisher Copyright:© 2015 SPIE.
Keywords
- Basis Pursuit
- Hyperspectral Compression
- Hyperspectral Imagery
- Online Learning
- Sparse Coding