{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:13:47Z","timestamp":1760242427767,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2017,8,14]],"date-time":"2017-08-14T00:00:00Z","timestamp":1502668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671342, U1609203, 41401389"],"award-info":[{"award-number":["41671342, U1609203, 41401389"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chinese Postdoctoral Science Foundation","award":["2015M570668,2016T90732"],"award-info":[{"award-number":["2015M570668,2016T90732"]}]},{"name":"Public Projects of Zhejiang Province","award":["2016C33021"],"award-info":[{"award-number":["2016C33021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A Probabilistic Weighted Archetypal Analysis method with Earth Mover\u2019s Distance (PWAA-EMD) is proposed to extract endmembers from hyperspectral imagery (HSI). The PWAA-EMD first utilizes the EMD dissimilarity matrix to weight the coefficient matrix in the regular Archetypal Analysis (AA). The EMD metric considers manifold structures of spectral signatures in the HSI data and could better quantify the dissimilarity features among pairwise pixels. Second, the PWAA-EMD adopts the Bayesian framework and formulates the improved AA into a probabilistic inference problem by maximizing a joint posterior density. Third, the optimization problem is solved by the iterative multiplicative update scheme, with a careful initialization from the two-stage algorithm and the proper endmembers are finally obtained. The synthetic and real Cuprite Hyperspectral datasets are utilized to verify the performance of PWAA-EMD and five popular methods are implemented to make comparisons. The results show that PWAA-EMD surpasses all the five methods in the average results of spectral angle distance (SAD) and root-mean-square-error (RMSE). Especially, the PWAA-EMD obtains more accurate estimation than AA in almost all the classes of endmembers including two similar ones. Therefore, the PWAA-EMD could be an alternative choice for endmember extraction on the hyperspectral data.<\/jats:p>","DOI":"10.3390\/rs9080841","type":"journal-article","created":{"date-parts":[[2017,8,14]],"date-time":"2017-08-14T10:23:12Z","timestamp":1502706192000},"page":"841","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Probabilistic Weighted Archetypal Analysis Method with Earth Mover\u2019s Distance for Endmember Extraction from Hyperspectral Imagery"],"prefix":"10.3390","volume":"9","author":[{"given":"Weiwei","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China"},{"name":"State Key Lab of Information Engineering on Survey, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Dianfa","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China"}]},{"given":"Yan","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA"}]},{"given":"Long","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA"}]},{"given":"Gang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China"}]},{"given":"Weiyue","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. 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