{"id":970,"date":"2013-06-11T10:40:03","date_gmt":"2013-06-11T15:40:03","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=970"},"modified":"2026-02-18T11:28:04","modified_gmt":"2026-02-18T16:28:04","slug":"zare2013beta","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2013\/06\/11\/zare2013beta\/","title":{"rendered":"Spectral unmixing using the beta compositional model"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>This paper introduces a beta compositional model as a mixing model for hyperspectral images. Endmembers are represented via beta distributions, hereafter referred to as betas, to constrain endmembers to a physically-meaningful range. Two associated spectral unmixing algorithms are described and applied to simulated and real hyperspectral imagery.<\/p>\n<h2>Links:<\/h2>\n<p> <a href=\"https:\/\/github.com\/GatorSense\/Publications\/blob\/master\/zare2013beta.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><a href=\"https:\/\/github.com\/GatorSense\/BetaCompositionalModel\"><img decoding=\"async\" border=\"2\" alt=\"Code\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/GitHub-Mark-e1482256611783.png\" height=\"50\"><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre><code>A. Zare, P. Gader, D. Dranishnikov, and T. Glenn, \u201cSpectral unmixing using the beta compositional model,\u201d in 5th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013.<\/code><\/pre>\n<pre><code>@InProceedings{zare2013beta,\nTitle = {Spectral unmixing using the beta compositional model},\nAuthor = {Zare, Alina and Gader, Paul and Dranishnikov, Dmitri and Glenn, Taylor},\nBooktitle = {5th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)},\nYear = {2013},\nMonth = {June},\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: This paper introduces a beta compositional model as a mixing model for hyperspectral images. Endmembers are represented via beta distributions, hereafter referred to as betas, to constrain endmembers to a physically-meaningful range. Two associated spectral unmixing algorithms are described and applied to simulated and real hyperspectral imagery. Links: Citation: A. Zare, P. Gader, D. [&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":[273,275,365,781],"class_list":["post-970","post","type-post","status-publish","format-standard","hentry","category-conference_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\/970","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=970"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/970\/revisions"}],"predecessor-version":[{"id":14655,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/970\/revisions\/14655"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=970"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=970"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=970"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}