Creator:

Ülkü, Írem ; Kizgut, Ersin

Contributor:

Aitouche, Abdel - ed.

Title:

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

Subtitle:

.

Group publication title:

AMCS, volume 28 (2018)

Subject and Keywords:

hyperspectral imaging ; compression algorithms ; dictionary learning ; sparse coding

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Date:

2018

Resource Type:

artykuł

DOI:

10.2478/amcs-2018-0015

Pages:

197-207

Source:

AMCS, volume 28, number 1 (2018) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego