{"id":520,"date":"2007-07-01T17:24:47","date_gmt":"2007-07-01T22:24:47","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=520"},"modified":"2026-02-18T11:28:09","modified_gmt":"2026-02-18T16:28:09","slug":"zare2007sparsitypromoting","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2007\/07\/01\/zare2007sparsitypromoting\/","title":{"rendered":"Sparsity promoting iterated constrained endmember detection in hyperspectral imagery"},"content":{"rendered":"

Abstract:<\/h2>\n

An extension of the iterated constrained endmember (ICE) algorithm that incorporates sparsity-promoting priors to find the correct number of endmembers is presented. In addition to solving for endmembers and endmember fractional maps, this algorithm attempts to autonomously determine the number of endmembers that are required for a particular scene. The number of endmembers is found by adding a sparsity-promoting term to ICE’s objective function.<\/p>\n

Links:<\/h2>\n

\"IEEE<\/a> \"PDF\"<\/a>\"Code\"<\/a>\"PyPI\"<\/a>\"CondaForge\"<\/a><\/p>\n

Citation:<\/h2>\n
A. Zare and P. Gader, \u201cSparsity promoting iterated constrained endmember detection in hyperspectral imagery,\u201d IEEE Geosci. Remote Sens. Lett., vol. 4, iss. 3, pp. 446-450, 2007. <\/code><\/pre>\n
@Article{zare2007sparsitypromoting,\nTitle = {Sparsity promoting iterated constrained endmember detection in hyperspectral imagery},\nAuthor = {Alina Zare and Paul Gader},\nJournal = {IEEE Geosci. Remote Sens. Lett.},\nYear = {2007},\nMonth = {July},\nNumber = {3},\nPages = {446 -450},\nVolume = {4},\nDoi = {10.1109\/LGRS.2007.895727}}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"
Abstract: An extension of the iterated constrained endmember (ICE) algorithm that incorporates sparsity-promoting priors to find the correct number of endmembers is presented. In addition to solving for endmembers and endmember fractional maps, this algorithm attempts to autonomously determine the number of endmembers that are required for a particular scene. The number of endmembers is […]<\/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":[273,365,681,781],"class_list":["post-520","post","type-post","status-publish","format-standard","hentry","category-journal_paper","tag-endmember","tag-hyperspectral","tag-sparsity-promotion","tag-unmixing"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/520","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=520"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/520\/revisions"}],"predecessor-version":[{"id":14725,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/520\/revisions\/14725"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=520"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=520"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=520"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}