Simsek, MuratPolat, Ediz2020-06-252020-06-252015closedAccess978-1-4673-8146-8https://hdl.handle.net/20.500.12587/6344International Conference Information Communication Automation Technologies (ICAT) -- OCT 29-31, 2015 -- Sarajevo, BOSNIA & HERCEGThe spatial resolutions of hyperspectral images are generally lower due to imaging hardware limitations. Super-resolution algorithms can be applied to obtain higher resolutions. Many algorithms exist to achieve super-resolution hyperspectral images from low resolution images acquired in different wavelengths. One of the popular algorithms is sparse representation-based algorithms that employ dictionary learning methods. In this study, a comparative framework is developed to investigate which dictionary learning algorithm leads to better super-resolution images. In order to achieve that, K-SVD and ODL dictionary learning algorithms are employed for comparison. A sparse representation-based algorithm G-SOMP+ is used for hyperspectral super-resolution reconstruction. The experimental results show that ODL algorithm outperforms K-SVD in terms of both reconstruction quality and processing times.eninfo:eu-repo/semantics/closedAccessHyperspectralsuper-resolutionsparse respresentationdictionary learningConference Object2-s2.0-84960935756N/AWOS:000380438700014N/A