LARGE-SCALE HYPERSPECTRAL IMAGE COMPRESSION VIA SPARSE REPRESENTATIONS BASED ON ONLINE LEARNING

Ulku İ., Kizgut E.

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE, cilt.28, sa.1, ss.197-207, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 28 Sayı: 1
  • Basım Tarihi: 2018
  • Doi Numarası: 10.2478/amcs-2018-0015
  • Dergi Adı: INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.197-207
  • Anahtar Kelimeler: hyperspectral imaging, compression algorithms, dictionary learning, sparse coding, SIGNAL RECOVERY, ALGORITHMS, PROJECTION
  • Ankara Üniversitesi Adresli: Hayır

Özet

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.