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Paper details
Number 1 - March 2018
Volume 28 - 2018
Large-scale hyperspectral image compression via sparse representations based on online learning
İrem Ülkü, Ersin Kizgut
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.
Keywords
hyperspectral imaging, compression algorithms, dictionary learning, sparse coding