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

Xiao Fu, Wing Kin Ma, Tsung Han Chan, Jose M. Bioucas-Dias, Marian Daniel Iordache

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

This paper studies a multiple-measurement vector (MMV)-based sparse regression approach to blind hyperspectral unmixing. 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 vectors. We tackle this self-dictionary MMV (SD-MMV) approach using greedy pursuit. It is shown that the resulting greedy algorithms are identical or very similar to some representative pure pixels identification algorithms, such as vertex component analysis. Hence, our study provides a new dimension on understanding and interpreting pure pixels identification methods. We also prove that in the noiseless case, the greedy SD-MMV algorithms guarantee perfect identification of pure pixels when the pure pixel assumption holds.

Original languageEnglish (US)
Title of host publication2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Print)9780992862602
StatePublished - Jan 1 2013
Event2013 21st European Signal Processing Conference, EUSIPCO 2013 - Marrakech, Morocco
Duration: Sep 9 2013Sep 13 2013

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Other

Other2013 21st European Signal Processing Conference, EUSIPCO 2013
Country/TerritoryMorocco
CityMarrakech
Period9/9/139/13/13

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