Toreyin, Behcet UgurUlku, Irem01. Çankaya Üniversitesi06. Mühendislik Fakültesi06.03. Elektrik-Elektronik Mühendisliği2017-03-092025-09-182017-03-092025-09-182015Ülkü, İ., Töreyin, B.U. (2015). Sparse coding of hyperspectral imagery using online learning. Signal Image And Video Processing, 9(4), 959-966. http://dx.doi.org/10.1007/s11760-015-0753-91863-17031863-1711https://doi.org/10.1007/s11760-015-0753-9https://hdl.handle.net/20.500.12416/13309Toreyin, Behcet Ugur/0000-0003-4406-2783Sparse 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.eninfo:eu-repo/semantics/closedAccessSparse CodingHyperspectral ImageryAnomaly DetectionOnline LearningArticle10.1007/s11760-015-0753-92-s2.0-84925500335