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Article: Multiple Algorithm Integration Based on Ant Colony Optimization for Endmember Extraction from Hyperspectral Imagery

TitleMultiple Algorithm Integration Based on Ant Colony Optimization for Endmember Extraction from Hyperspectral Imagery
Authors
KeywordsAnt colony optimization (ACO)
hyperspectral imagery
multiple algorithm integration
endmember extraction
Issue Date2015
Citation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, v. 8, n. 6, p. 2569-2582 How to Cite?
AbstractSpectral unmixing is an important technique in hyperspectral image exploitation. It comprises the extraction of a set of pure spectral signatures (called endmembers in hyperspectral jargon) and their corresponding fractional abundances in each pixel of the scene. Over the last few years, many approaches have been proposed to automatically extract endmembers, which is a critical step of the spectral unmixing chain. Recently, ant colony optimization (ACO) techniques have reformulated the endmember extraction issue as a combinatorial optimization problem. Due to the huge computation load involved, how to provide suitable candidate endmembers for ACO is particularly important, but this aspect has never been discussed before in the literature. In this paper, we illustrate the capacity of ACO techniques for integrating the results obtained by different endmember extraction algorithms. Our experimental results, conducted using several state-of-the-art endmember extraction approaches using both simulated and a real hyperspectral scene (cuprite), indicate that the proposed ACO-based strategy can provide endmembers which are robust against noise and outliers.
Persistent Identifierhttp://hdl.handle.net/10722/298223
ISSN
2020 Impact Factor: 3.784
2020 SCImago Journal Rankings: 1.246

 

DC FieldValueLanguage
dc.contributor.authorGao, Lianru-
dc.contributor.authorGao, Jianwei-
dc.contributor.authorLi, Jun-
dc.contributor.authorPlaza, Antonio-
dc.contributor.authorZhuang, Lina-
dc.contributor.authorSun, Xu-
dc.contributor.authorZhang, Bing-
dc.date.accessioned2021-04-08T03:07:56Z-
dc.date.available2021-04-08T03:07:56Z-
dc.date.issued2015-
dc.identifier.citationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, v. 8, n. 6, p. 2569-2582-
dc.identifier.issn1939-1404-
dc.identifier.urihttp://hdl.handle.net/10722/298223-
dc.description.abstractSpectral unmixing is an important technique in hyperspectral image exploitation. It comprises the extraction of a set of pure spectral signatures (called endmembers in hyperspectral jargon) and their corresponding fractional abundances in each pixel of the scene. Over the last few years, many approaches have been proposed to automatically extract endmembers, which is a critical step of the spectral unmixing chain. Recently, ant colony optimization (ACO) techniques have reformulated the endmember extraction issue as a combinatorial optimization problem. Due to the huge computation load involved, how to provide suitable candidate endmembers for ACO is particularly important, but this aspect has never been discussed before in the literature. In this paper, we illustrate the capacity of ACO techniques for integrating the results obtained by different endmember extraction algorithms. Our experimental results, conducted using several state-of-the-art endmember extraction approaches using both simulated and a real hyperspectral scene (cuprite), indicate that the proposed ACO-based strategy can provide endmembers which are robust against noise and outliers.-
dc.languageeng-
dc.relation.ispartofIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing-
dc.subjectAnt colony optimization (ACO)-
dc.subjecthyperspectral imagery-
dc.subjectmultiple algorithm integration-
dc.subjectendmember extraction-
dc.titleMultiple Algorithm Integration Based on Ant Colony Optimization for Endmember Extraction from Hyperspectral Imagery-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/JSTARS.2014.2371615-
dc.identifier.scopuseid_2-s2.0-85027929781-
dc.identifier.volume8-
dc.identifier.issue6-
dc.identifier.spage2569-
dc.identifier.epage2582-
dc.identifier.eissn2151-1535-
dc.identifier.issnl1939-1404-

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