Simsek, MuratPolat, Ediz2020-06-252020-06-252016closedAccess978-1-5090-1679-2https://hdl.handle.net/20.500.12587/666924th Signal Processing and Communication Application Conference (SIU) -- MAY 16-19, 2016 -- Zonguldak, TURKEYDue to hardware limitations, hyperspectral imagery has low spatial resolution. It can be obtained super-resolution hyperspectral imagery by means of sparse representation-based methods that are designed for improving spatial resolution. In this paper, the effect of sparse representation-based dictionary learning algorithms including K-SVD, ODL and Bayes on obtaining superresolution images with low error and high quality has been investigated. The method with best results has been identified.trinfo:eu-repo/semantics/closedAccesshyperspectral imagessuper resolutionsparse representationdictionary learningConference Object753756WOS:000391250900166N/A