{"id":726,"date":"2010-06-10T22:41:00","date_gmt":"2010-06-11T03:41:00","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=726"},"modified":"2026-02-18T11:28:08","modified_gmt":"2026-02-18T16:28:08","slug":"zare2010spatially","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2010\/06\/10\/zare2010spatially\/","title":{"rendered":"Spatially-smooth piece-wise convex endmember detection"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>An endmember detection and spectral unmixing algorithm that uses both spatial and spectral information is presented. This method, Spatial Piece-wise Convex Multiple Model Endmember Detection (Spatial P-COMMEND), autonomously estimates multiple sets of endmembers and performs spectral unmixing for input hyperspectral data. Spatial P-COMMEND does not restrict the estimated endmembers to define a single convex region during spectral unmixing. Instead, a piece-wise convex representation is used that can effectively represent non-convex hyperspectral data. Spatial P-COMMEND drives neighboring pixels to be unmixed by the same set of endmembers encouraging spatially-smooth unmixing results.<\/p>\n<h2>Links:<\/h2>\n<p> <a href=\"http:\/\/dx.doi.org\/10.1109\/WHISPERS.2010.5594897\"><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\/zare2010spatially.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, O. Bchir, H. Frigui, and P. Gader, \u201cSpatially-smooth piece-wise convex endmember detection,\u201d in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010. <\/code><\/pre>\n<pre><code>@InProceedings{zare2010spatially,\nTitle = {Spatially-smooth piece-wise convex endmember detection},\nAuthor = {Alina Zare and Ouiem Bchir and Hichem Frigui and Paul Gader},\nBooktitle = {2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)},\nYear = {2010},\nMonth = {June},\nDoi = {10.1109\/WHISPERS.2010.5594897},\n}<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: An endmember detection and spectral unmixing algorithm that uses both spatial and spectral information is presented. This method, Spatial Piece-wise Convex Multiple Model Endmember Detection (Spatial P-COMMEND), autonomously estimates multiple sets of endmembers and performs spectral unmixing for input hyperspectral data. Spatial P-COMMEND does not restrict the estimated endmembers to define a single convex [&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":[17],"tags":[273,365,659,683,781],"class_list":["post-726","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-endmember","tag-hyperspectral","tag-segmentation","tag-spatial","tag-unmixing"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/726","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=726"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/726\/revisions"}],"predecessor-version":[{"id":14707,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/726\/revisions\/14707"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=726"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=726"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=726"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}