Abstract
A lossy hyperspectral image compression method is proposed using online learning based sparse coding. The least number of coefficients are obtained to represent hyperspectral images by applying the sparse coding algorithm which is based on a dicriminative online dictionary learning method. Results indicate that a pre-analysis of the number of non-zero dictionary elements may help in improving the overall compression quality.
Original language | English |
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Title of host publication | 2014 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781479979714 |
DOIs | |
Publication status | Published - 13 Jan 2014 |
Externally published | Yes |
Event | 2014 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014 - Paris, France Duration: 1 Nov 2014 → 2 Nov 2014 |
Publication series
Name | 2014 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014 |
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Conference
Conference | 2014 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014 |
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Country/Territory | France |
City | Paris |
Period | 1/11/14 → 2/11/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
Keywords
- Anomaly Detection
- Hyperspectral Imagery
- Online Learning
- Sparse Coding