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Article: A quantitative and comparative analysis of different preprocessing implementations of DPSO: a robust endmember extraction algorithm

TitleA quantitative and comparative analysis of different preprocessing implementations of DPSO: a robust endmember extraction algorithm
Authors
KeywordsHyperspectral remote sensing
Preprocessing implementation
Discrete particle swarm optimization
Endmember extraction
Issue Date2016
Citation
Soft Computing, 2016, v. 20, n. 12, p. 4669-4683 How to Cite?
AbstractLinear spectral unmixing is a very important technique in hyperspectral image analysis. It contains two main steps. First, it finds spectrally unique signatures of pure ground components (called endmembers); second, it estimates their corresponding fractional abundances in each pixel. Recently, a discrete particle swarm optimization (DPSO) algorithm was introduced to accurately extract endmembers with high optimal performance. However, because of its limited feasible solution space, DPSO necessarily needs a small amount of candidate endmembers before extraction. Consequently, how to provide a suitable candidate endmember set, which has not been analyzed yet, is a critical issue in using DPSO for unmixing problem. In this study, three representative pure pixel-based methods, pixel purity index, vertex component analysis (VCA), and N-FINDR, are quantitatively compared to provide candidate endmembers for DPSO. The experiments with synthetic and real hyperspectral images indicate that VCA is the most reliable preprocessing implementation for DPSO. Further, it can be concluded that DPSO with the proposed preprocessing implementations given in this paper is robust for endmember extraction.
Persistent Identifierhttp://hdl.handle.net/10722/298097
ISSN
2020 Impact Factor: 3.643
2020 SCImago Journal Rankings: 0.626
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorGao, Lianru-
dc.contributor.authorZhuang, Lina-
dc.contributor.authorWu, Yuanfeng-
dc.contributor.authorSun, Xu-
dc.contributor.authorZhang, Bing-
dc.date.accessioned2021-04-08T03:07:40Z-
dc.date.available2021-04-08T03:07:40Z-
dc.date.issued2016-
dc.identifier.citationSoft Computing, 2016, v. 20, n. 12, p. 4669-4683-
dc.identifier.issn1432-7643-
dc.identifier.urihttp://hdl.handle.net/10722/298097-
dc.description.abstractLinear spectral unmixing is a very important technique in hyperspectral image analysis. It contains two main steps. First, it finds spectrally unique signatures of pure ground components (called endmembers); second, it estimates their corresponding fractional abundances in each pixel. Recently, a discrete particle swarm optimization (DPSO) algorithm was introduced to accurately extract endmembers with high optimal performance. However, because of its limited feasible solution space, DPSO necessarily needs a small amount of candidate endmembers before extraction. Consequently, how to provide a suitable candidate endmember set, which has not been analyzed yet, is a critical issue in using DPSO for unmixing problem. In this study, three representative pure pixel-based methods, pixel purity index, vertex component analysis (VCA), and N-FINDR, are quantitatively compared to provide candidate endmembers for DPSO. The experiments with synthetic and real hyperspectral images indicate that VCA is the most reliable preprocessing implementation for DPSO. Further, it can be concluded that DPSO with the proposed preprocessing implementations given in this paper is robust for endmember extraction.-
dc.languageeng-
dc.relation.ispartofSoft Computing-
dc.subjectHyperspectral remote sensing-
dc.subjectPreprocessing implementation-
dc.subjectDiscrete particle swarm optimization-
dc.subjectEndmember extraction-
dc.titleA quantitative and comparative analysis of different preprocessing implementations of DPSO: a robust endmember extraction algorithm-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s00500-014-1507-2-
dc.identifier.scopuseid_2-s2.0-84910028640-
dc.identifier.volume20-
dc.identifier.issue12-
dc.identifier.spage4669-
dc.identifier.epage4683-
dc.identifier.eissn1433-7479-
dc.identifier.isiWOS:000386611200005-
dc.identifier.issnl1432-7643-

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