{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:40:43Z","timestamp":1770918043450,"version":"3.50.1"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2016,3,1]],"date-time":"2016-03-01T00:00:00Z","timestamp":1456790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"crossref","award":["307071\/2013-8"],"award-info":[{"award-number":["307071\/2013-8"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"crossref","award":["400566\/2013-3"],"award-info":[{"award-number":["400566\/2013-3"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"crossref","award":["474735\/2012-5"],"award-info":[{"award-number":["474735\/2012-5"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Agence Nationale de la Recherche (ANR) through the Hypanema Project","award":["ANR-12-BS03-003"],"award-info":[{"award-number":["ANR-12-BS03-003"]}]},{"name":"Centre National de la Recherche Scientifique Imag\u2019in Project","award":["2015OPTIMISME"],"award-info":[{"award-number":["2015OPTIMISME"]}]},{"name":"Agence Nationale de la Recherche (ANR) through the Hypanema Project","award":["ANR-12-BS03-003"],"award-info":[{"award-number":["ANR-12-BS03-003"]}]},{"name":"Bayesian nonparametric methods for signal and image processing ANR Project","award":["ANR-13-BS-03-0006-01"],"award-info":[{"award-number":["ANR-13-BS-03-0006-01"]}]},{"name":"Agence Nationale de la Recherche (ANR) through the Hypanema Project","award":["ANR-12-BS03-003"],"award-info":[{"award-number":["ANR-12-BS03-003"]}]},{"name":"ANR Project","award":["ANR-11-LABX-0040-CIMI"],"award-info":[{"award-number":["ANR-11-LABX-0040-CIMI"]}]},{"name":"ANR Project","award":["ANR-11-IDEX-0002-02"],"award-info":[{"award-number":["ANR-11-IDEX-0002-02"]}]},{"name":"Agence Nationale de la Recherche (ANR) through the Hypanema Project","award":["ANR-12-BS03-003"],"award-info":[{"award-number":["ANR-12-BS03-003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2016,3]]},"DOI":"10.1109\/tip.2015.2509258","type":"journal-article","created":{"date-parts":[[2015,12,17]],"date-time":"2015-12-17T14:40:59Z","timestamp":1450363259000},"page":"1136-1151","source":"Crossref","is-referenced-by-count":31,"title":["Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images"],"prefix":"10.1109","volume":"25","author":[{"given":"Tales","family":"Imbiriba","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose Carlos Moreira","family":"Bermudez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cedric","family":"Richard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Yves","family":"Tourneret","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2006.888466"},{"key":"ref38","first-page":"1","article-title":"GLUP: Yet another algorithm for blind unmixing of hyperspectral data","author":"ammanouil","year":"2014","journal-title":"Proc IEEE WHISPERS"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.226"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2014.6854184"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638034"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2210235"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2362056"},{"key":"ref36","first-page":"21","article-title":"Automatic endmember extraction from hyperspectral data for mineral exploration","author":"neville","year":"1999","journal-title":"Proc 21st Can Symp Remote Sens"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1117\/12.366289","article-title":"N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data","volume":"3753","author":"winter","year":"1999","journal-title":"Proc SPIE"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2011.2170999"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2011.2109367"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2446196"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2245127"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2010.2088377"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/0034-4257(94)90107-4"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2013.2244672"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2014.6855148"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/WHISPERS.2009.5289073"},{"key":"ref22","first-page":"11","article-title":"Automated spectral unmixing of AVIRIS data using convex geometry concepts","volume":"1","author":"boardman","year":"1993","journal-title":"Proc AVIRIS Workshop"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2312616"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2006.881803"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2005.844293"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2011.2141672"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2009.2025802"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2003.819189"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2008.918089"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2009.2025797"},{"key":"ref58","doi-asserted-by":"crossref","first-page":"5131","DOI":"10.1029\/2002JE001847","article-title":"Imaging spectroscopy: Earth and planetary remote sensing with the usgs tetracorder and expert systems","volume":"108","author":"clark","year":"2003","journal-title":"J Geophys Res"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71972-4"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/36.911111"},{"key":"ref55","year":"2013","journal-title":"ENVI User&#x2019;s Guide Version 4 0"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/s10898-012-9876-5"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2011.2151197"},{"key":"ref52","article-title":"Estimating the intrinsic dimension of hyperspectral images using an eigen-gap approach","author":"halimi","year":"2015"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/36.957296"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1080\/01431160802558659"},{"key":"ref40","first-page":"245","article-title":"Advances in nonlinear blind source separation","author":"jutten","year":"2003","journal-title":"Proc ICA"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2011.6049491"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2012.2222390"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/WHISPERS.2012.6874231"},{"key":"ref15","first-page":"1","article-title":"Estimating abundance fractions of materials in hyperspectral images by fitting a post-nonlinear mixing model","author":"chen","year":"2013","journal-title":"Proc IEEE WHISPERS"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2013.2264392"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2187668"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2010.2098414"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2007.4423736"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/79.974718"},{"key":"ref3","first-page":"859","article-title":"The evolution of landsat data analysis","volume":"63","author":"landgrebe","year":"1997","journal-title":"Photogram Eng Remote Sens"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2013.2279274"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/79.974727"},{"key":"ref8","first-page":"74770i-1","article-title":"Nonlinear mixture model for hyperspectral unmixing","volume":"7477","author":"nascimento","year":"2009","journal-title":"Proc SPIE"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/0034-4257(95)00171-9"},{"key":"ref49","first-page":"3086","article-title":"Hyperspectral image unmixing using manifold learning methods derivations and comparative tests","author":"nguyen","year":"2012","journal-title":"Proc IEEE IGARSS"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2009.02.003"},{"key":"ref46","author":"kay","year":"2011","journal-title":"Fundamentals statistical signal processing detection theory"},{"key":"ref45","author":"papoulis","year":"2006","journal-title":"Probability random variables and stochastic processes"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2314022"},{"key":"ref47","volume":"1","author":"johnson","year":"1994","journal-title":"Continuous Univariate Distributions"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511524998"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2011.5946577"},{"key":"ref44","author":"rasmussen","year":"2015","journal-title":"Gaussia Processes for Machine Learning"},{"key":"ref43","author":"rasmussen","year":"2006","journal-title":"Gaussian Processes for Machine Learning"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/7383373\/07358107.pdf?arnumber=7358107","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T11:46:39Z","timestamp":1641987999000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7358107\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,3]]},"references-count":61,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tip.2015.2509258","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,3]]}}}