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

• Autorzy: Ülkü İ. , Kizgut E.

Identyfikatory

DOI

10.2478/amcs-2018-0015

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

hyperspectral imaging; compression algorithm; dictionary learning; sparse coding

PL

obrazowanie wielospektralne; algorytm kompresji; nauczanie online; kodowanie rzadkie

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2018
• Tom: Vol. 28, no. 1
• Strony: 197--207
• Opis fizyczny: Bibliogr. 34 poz., rys., tab., wykr.

Twórcy

autor

Ülkü İ.

• Department of Electrical and Electronics Engineering, Çankaya University, Eskişehir Yolu 29.km, 06790 Ankara, Turkey

autor

Kizgut E.

• University Institute of Pure and Applied Mathematics (IUMPA), Technical University of Valencia, E-46071 Valencia, Spain

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).