Sparse coding of hyperspectral imagery using online learning

Ulku İ., Toreyin B. U.

SIGNAL IMAGE AND VIDEO PROCESSING, vol.9, no.4, pp.959-966, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9 Issue: 4
  • Publication Date: 2015
  • Doi Number: 10.1007/s11760-015-0753-9
  • Journal Name: SIGNAL IMAGE AND VIDEO PROCESSING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.959-966
  • Keywords: Sparse coding, Hyperspectral imagery, Anomaly detection, Online learning, COMPRESSION, CLASSIFICATION
  • Ankara University Affiliated: No

Abstract

Sparse coding ensures to express the data in terms of a few nonzero dictionary elements. Since the data size is large for hyperspectral imagery, it is reasonable to use sparse coding for compression of hyperspectral images. In this paper, a hyperspectral image compression method is proposed using a discriminative online learning-based sparse coding algorithm. Compression and anomaly detection tests are performed on hyperspectral images from the AVIRIS dataset. Comparative rate-distortion analyses indicate that the proposed method is superior to the state-of-the-art hyperspectral compression techniques.