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

İrem Ülkü; Ersin Kizgut

doi:10.2478/amcs-2018-0015

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

International Journal of Applied Mathematics and Computer Science

Volume 28 (2018): Issue 1 (March 2018)

By: İrem Ülkü and Ersin Kizgut

Open Access

|Mar 2018

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.

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Articles in this issue

DOI: https://doi.org/10.2478/amcs-2018-0015 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X

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Language: English

Page range: 197 - 207

Submitted on: Feb 10, 2017

Accepted on: Oct 16, 2017

Published on: Mar 31, 2018

Published by: University of Zielona Góra

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

hyperspectral imaging,

compression algorithms,

Related subjects:

© 2018 İrem Ülkü, Ersin Kizgut, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 28 (2018): Issue 1 (March 2018)