{"id":852,"date":"2012-10-11T08:14:31","date_gmt":"2012-10-11T13:14:31","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=852"},"modified":"2026-02-18T11:28:05","modified_gmt":"2026-02-18T16:28:05","slug":"zare2012hyperspectral","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2012\/10\/11\/zare2012hyperspectral\/","title":{"rendered":"Hyperspectral image analysis with piece-wise convex endmember estimation and spectral unmixing"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmembers is presented. This algorithm, the Piece-wise Convex Multiple Model Endmember Detection (P-COMMEND) algorithm, models a hyperspectral image using a piece-wise convex representation. By using a piece-wise convex representation, non-convex hyperspectral data are more accurately characterized. For example, the well-known Indian Pines hyperspectral image is used as an example of a piece-wise convex collection of pixels. The convex regions, weights, endmembers and abundances are found using an iterative fuzzy clustering method. Results indicate that the piece-wise convex representation provides endmembers that better represent hyperspectral data sets over methods that use a single convex region.<\/p>\n<h2>Links:<\/h2>\n<p> <a href=\"http:\/\/doi.org\/10.1109\/ICIP.2012.6467451\"><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\/zare2012hyperspectral.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, \u201cHyperspectral image analysis with piece-wise convex endmember estimation and spectral unmixing,\u201d in 19th IEEE Int. Conf. Image Proc. (ICIP), 2012, pp. 2681-2684.<\/code><\/pre>\n<pre><code>@InProceedings{zare2012hyperspectral,\nauthor = {Alina Zare and Ouiem Bchir and Hichem Frigui and Paul Gader},\ntitle = {Hyperspectral image analysis with piece-wise convex endmember estimation and spectral unmixing},\nbooktitle = {19th IEEE Int. Conf. Image Proc. (ICIP)},\nyear = {2012},\npages = {2681-2684},\nmonth = {Oct.},\ndoi = {10.1109\/ICIP.2012.6467451},\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmembers is presented. This algorithm, the Piece-wise Convex Multiple Model Endmember Detection (P-COMMEND) algorithm, models a hyperspectral image using a piece-wise convex representation. By using a piece-wise convex representation, non-convex hyperspectral data are more accurately characterized. For example, the well-known Indian [&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":[273,365,659,781],"class_list":["post-852","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-endmember","tag-hyperspectral","tag-segmentation","tag-unmixing"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/852","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=852"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/852\/revisions"}],"predecessor-version":[{"id":14669,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/852\/revisions\/14669"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=852"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=852"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=852"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}