Greedy algorithms for pure pixels identification in hyperspectral unmixing: A multiple-measurement vector viewpoint

Abstract

This paper studies a multiple-measurement vector (MMV)-based sparse regression approach to blind hyperspectral un-mixing. In general, sparse regression requires a dictionary. The considered approach uses the measured hyperspectral data as the dictionary, thereby intending to represent the whole measured data using the fewest number of measured hyperspectral… (More)

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@article{Fu2013GreedyAF, title={Greedy algorithms for pure pixels identification in hyperspectral unmixing: A multiple-measurement vector viewpoint}, author={Xiao Fu and Wing-Kin Ma and Tsung-Han Chan and Jos{\'e} M. Bioucas-Dias and Marian-Daniel Iordache}, journal={21st European Signal Processing Conference (EUSIPCO 2013)}, year={2013}, pages={1-5} }