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Article: Detection of yellow starthistle through band selection and feature extraction from hyperspectral imagery

TitleDetection of yellow starthistle through band selection and feature extraction from hyperspectral imagery
Authors
Issue Date2007
Citation
Photogrammetric Engineering and Remote Sensing, 2007, v. 73, n. 9, p. 1005-1015 How to Cite?
AbstractTo effectively display hyperspectral imagery for visualization purposes, the three RGB channels should be selected or extracted from a hyperspectral image under the criteria of maximum information or maximum between-class separability. Seven band selection (OIF, SI, CI, divergence, transformed divergence, B-distance, JM-distance) and five feature extraction (principal component analysis, linear discriminant analysis, class-based PCA, segmented PCT (SPCT), independent component analysis) methods and their variations are examined and compared using CASI hyperspectral imagery with the goal of detecting Centaurea solstitialis (yellow starthistle or YST), an invasive weed, in an annual grassland in California. Three indicators, information index (Infodex), separability index (Sepadex) and average correlation coefficient (ACC) are proposed to evaluate the quality of the generated images. The results suggest that both the combination of the three SPCT channels and the combination of the second PCA channel with the positive and negative of the first LDA channels (PCA2, LDA1, -LDA1) can enhance our ability to visualize the distribution of YST in contrast to the surrounding vegetation. © 2007 American Society for Photogrammetry and Remote Sensing.
Persistent Identifierhttp://hdl.handle.net/10722/296616
ISSN
2023 Impact Factor: 1.0
2023 SCImago Journal Rankings: 0.309
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorMiao, Xin-
dc.contributor.authorGong, Peng-
dc.contributor.authorSwope, Sarah-
dc.contributor.authorPu, Ruiliang-
dc.contributor.authorCarruthers, Raymond-
dc.contributor.authorAnderson, Gerald L.-
dc.date.accessioned2021-02-25T15:16:17Z-
dc.date.available2021-02-25T15:16:17Z-
dc.date.issued2007-
dc.identifier.citationPhotogrammetric Engineering and Remote Sensing, 2007, v. 73, n. 9, p. 1005-1015-
dc.identifier.issn0099-1112-
dc.identifier.urihttp://hdl.handle.net/10722/296616-
dc.description.abstractTo effectively display hyperspectral imagery for visualization purposes, the three RGB channels should be selected or extracted from a hyperspectral image under the criteria of maximum information or maximum between-class separability. Seven band selection (OIF, SI, CI, divergence, transformed divergence, B-distance, JM-distance) and five feature extraction (principal component analysis, linear discriminant analysis, class-based PCA, segmented PCT (SPCT), independent component analysis) methods and their variations are examined and compared using CASI hyperspectral imagery with the goal of detecting Centaurea solstitialis (yellow starthistle or YST), an invasive weed, in an annual grassland in California. Three indicators, information index (Infodex), separability index (Sepadex) and average correlation coefficient (ACC) are proposed to evaluate the quality of the generated images. The results suggest that both the combination of the three SPCT channels and the combination of the second PCA channel with the positive and negative of the first LDA channels (PCA2, LDA1, -LDA1) can enhance our ability to visualize the distribution of YST in contrast to the surrounding vegetation. © 2007 American Society for Photogrammetry and Remote Sensing.-
dc.languageeng-
dc.relation.ispartofPhotogrammetric Engineering and Remote Sensing-
dc.titleDetection of yellow starthistle through band selection and feature extraction from hyperspectral imagery-
dc.typeArticle-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.scopuseid_2-s2.0-36649033329-
dc.identifier.volume73-
dc.identifier.issue9-
dc.identifier.spage1005-
dc.identifier.epage1015-
dc.identifier.isiWOS:000249234700003-
dc.identifier.issnl0099-1112-

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