{"id":1074,"date":"2014-01-11T14:25:57","date_gmt":"2014-01-11T19:25:57","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=1074"},"modified":"2026-02-18T11:28:03","modified_gmt":"2026-02-18T16:28:03","slug":"zare2014endmember","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2014\/01\/11\/zare2014endmember\/","title":{"rendered":"Endmember variability in hyperspectral analysis: addressing spectral variability during spectral unmixing"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>Variable illumination and environmental, atmospheric, and temporal conditions cause the measured spectral signature for a material to vary within hyperspectral imagery. By ignoring these variations, errors are introduced and propagated throughout hyperspectral image analysis. To develop accurate spectral unmixing and endmember estimation methods, a number of approaches that account for spectral variability have been developed. This article motivates and provides a review for methods that account for spectral variability during hyperspectral unmixing and endmember estimation and a discussion on topics for future work in this area.<\/p>\n<h2>Links:<\/h2>\n<p> <a href=\"http:\/\/doi.org\/10.1109\/MSP.2013.2279177\"><img decoding=\"async\" border=\"2\" alt=\"IEEE Link\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/ieee.jpg\" height=\"50\"><\/a> <a href=\"https:\/\/github.com\/GatorSense\/Publications\/blob\/master\/zare2014endmember.pdf\"><img decoding=\"async\" border=\"2\" alt=\"PDF\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/pdflogo-e1482256801729.png\" height=\"50\"><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre><code>A. Zare and K. C. Ho, \u201cEndmember variability in hyperspectral analysis: addressing spectral variability during spectral unmixing,\u201d IEEE Signal Process. Mag., vol. 31, iss. 1, pp. 95-104, 2014. <\/code><\/pre>\n<pre><code>@Article{zare2014endmember,\nTitle = {Endmember variability in hyperspectral analysis: addressing spectral variability during spectral unmixing},\nAuthor = {Zare, Alina and Ho, K. C.},\nJournal = {IEEE Signal Process. Mag.},\nYear = {2014},\nMonth = {Jan.},\nNumber = {1},\nPages = {95-104},\nVolume = {31},\nDoi = {10.1109\/MSP.2013.2279177},\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: Variable illumination and environmental, atmospheric, and temporal conditions cause the measured spectral signature for a material to vary within hyperspectral imagery. By ignoring these variations, errors are introduced and propagated throughout hyperspectral image analysis. To develop accurate spectral unmixing and endmember estimation methods, a number of approaches that account for spectral variability have been [&hellip;]<\/p>\n","protected":false},"author":28,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-templates\/single-sidebar-none.php","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[19],"tags":[273,275,365,781],"class_list":["post-1074","post","type-post","status-publish","format-standard","hentry","category-journal_paper","tag-endmember","tag-endmember-variability","tag-hyperspectral","tag-unmixing"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1074","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/comments?post=1074"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1074\/revisions"}],"predecessor-version":[{"id":14637,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1074\/revisions\/14637"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=1074"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=1074"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=1074"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}