<abstract xmlns="http://www.w3.org/1999/xhtml"><p> In this study, proximity based optimization algorithms are used for lossy compression of hyperspectral images that are inherently large scale. This is the first time that such proximity based optimization algorithms are implemented with an online dictionary learning method. Compression performances are compared with the one obtained by various sparse representation algorithms. As a result, proximity based optimization algorithms are listed among the three best ones in terms of compression performance values for all hyperspectral images. Additionally, the applicability of anomaly detection is tested on the reconstructed images.</p></abstract>

In this study, proximity based optimization algorithms are used for lossy compression of hyperspectral images that are inherently large scale. This is the first time that such proximity based optimization algorithms are implemented with an online dictionary learning method. Compression performances are compared with the one obtained by various sparse representation algorithms. As a result, proximity based optimization algorithms are listed among the three best ones in terms of compression performance values for all hyperspectral images. Additionally, the applicability of anomaly detection is tested on the reconstructed images.

Large-scale hyperspectral image compression via sparse representations based on online learning

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

In this study, proximity based optimization algorithms are used for lossy compression of hyperspectral images that are inherently large scale. This is the...

Large-scale hyperspectral image compression via sparse representations based on online learning

Online veröffentlicht: 31. März 2018

Seitenbereich: 197 - 207

Eingereicht: 10. Feb. 2017

Akzeptiert: 16. Okt. 2017

DOI: https://doi.org/10.2478/amcs-2018-0015

Schlüsselwörter
hyperspectral imaging, compression algorithms, dictionary learning, sparse coding

© by İrem Ülkü

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Large-scale hyperspectral image compression via sparse representations based on online learning

Online veröffentlicht: 31. März 2018

Seitenbereich: 197 - 207

Eingereicht: 10. Feb. 2017

Akzeptiert: 16. Okt. 2017

DOI: https://doi.org/10.2478/amcs-2018-0015

Schlüsselwörterhyperspectral imaging, compression algorithms, dictionary learning, sparse coding

© by İrem Ülkü

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
hyperspectral imaging, compression algorithms, dictionary learning, sparse coding