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Proceedings Paper

Hyperspectral image compression using an online learning method
Author(s): İrem Ülkü; B. Uğur Töreyin
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Paper 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.

Paper Details

Date Published: 21 May 2015
PDF: 11 pages
Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 950104 (21 May 2015); doi: 10.1117/12.2178133
Show Author Affiliations
İrem Ülkü, Çankaya Univ. (Turkey)
B. Uğur Töreyin, Çankaya Univ. (Turkey)
TÜBİTAK UZAY (Turkey)


Published in SPIE Proceedings Vol. 9501:
Satellite Data Compression, Communications, and Processing XI
Bormin Huang; Chein-I Chang; Chulhee Lee; Yunsong Li; Qian Du, Editor(s)

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