Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6421
Title: Compressed Sensing Based Hyperspectral Unmixing
Authors: Albayrak, R. Tufan
Gürbüz, Ali Cafer
Gunyel, Bertan
Keywords: Hyperspecytral unmixing
compressive sensing
sparsity
convex optimization
Issue Date: 2014
Publisher: IEEE
Source: 22nd IEEE Signal Processing and Communications Applications Conference (SIU) -- APR 23-25, 2014 -- Karadeniz Teknik Univ, Trabzon, TURKEY
Series/Report no.: Signal Processing and Communications Applications Conference
Abstract: In hyperspectral images the measured spectra for each pixel can be modeled as convex combination of small number of endmember spectra. Since the measured structure contains only a few of possible responses out of possibly many materials sparsity based convex optimization techniques or compressive sensing can be used for hyperspectral unmixing. In this work varying sparsity based techniques are tested for hyperspectral unmixing problem. Performance analysis of these techniques on sparsity level and measurement number are performed. Effect of high coherence of hyperspectral dictionaries is disccussed and effect of signal to noise ratio is analyzed.
URI: https://hdl.handle.net/20.500.11851/6421
ISBN: 978-1-4799-4874-1
ISSN: 2165-0608
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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