Hyperspectral image compression based on online learning spectral features dictionary

Hyperspectral image compression based on online learning spectral features dictionary This paper proposes a novel method of lossy hyperspectral image compression using online learning dictionary. Spectral dictionary that learned in sparse coding mode could be used to represent the corresponding material. From the perspective of sparse coding, learning a sparse dictionary could achieve a better result of data decorrelation. In order to compress the hyperspectral data, an online learning sparse coding dictionary which could describe the characteristics of spectral curve was created to represent and reconstruct hyperspectral data. In the online learning phase, effective clustering algorithm is applied to generate and update the dictionary more properly. Results indicate that dictionary achieved by our method could improve the compression quality of hyperspectral image observably. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Hyperspectral image compression based on online learning spectral features dictionary

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4724-8
Publisher site
See Article on Publisher Site

Abstract

This paper proposes a novel method of lossy hyperspectral image compression using online learning dictionary. Spectral dictionary that learned in sparse coding mode could be used to represent the corresponding material. From the perspective of sparse coding, learning a sparse dictionary could achieve a better result of data decorrelation. In order to compress the hyperspectral data, an online learning sparse coding dictionary which could describe the characteristics of spectral curve was created to represent and reconstruct hyperspectral data. In the online learning phase, effective clustering algorithm is applied to generate and update the dictionary more properly. Results indicate that dictionary achieved by our method could improve the compression quality of hyperspectral image observably.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: May 10, 2017

References

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