The Effect of Dictionary Learning Algorithms on Super-resolution Hyperspectral Reconstruction
Yükleniyor...
Tarih
2015
Yazarlar
Dergi Başlığı
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Cilt Başlığı
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Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The 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.
Açıklama
International Conference Information Communication Automation Technologies (ICAT) -- OCT 29-31, 2015 -- Sarajevo, BOSNIA & HERCEG
Anahtar Kelimeler
Hyperspectral, super-resolution, sparse respresentation, dictionary learning
Kaynak
2015 Xxv International Conference On Information, Communication And Automation Technologies (Icat)
WoS Q DeÄŸeri
N/A
Scopus Q DeÄŸeri
N/A
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Künye
closedAccess
