{"id":1034,"date":"2013-09-11T11:22:55","date_gmt":"2013-09-11T16:22:55","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=1034"},"modified":"2026-02-18T11:28:03","modified_gmt":"2026-02-18T16:28:03","slug":"dranishnikov2013unmixing","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2013\/09\/11\/dranishnikov2013unmixing\/","title":{"rendered":"Unmixing using a combined microscopic and macroscopic mixture model with distinct endmembers"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>Much work in the study of hyperspectral imagery has focused on macroscopic mixtures and unmixing via the linear mixing model. A substantially different approach seeks to model hyperspectral data non-linearly in order to accurately describe intimate or microscopic relationships of materials within the image. In this paper we present and discuss a new model (MacMicDEM) that seeks to unify both approaches by representing a pixel as both linearly and non-linearly mixed, with the condition that the endmembers for both mixture types need not be related. Using this model, we develop a method to accurately and quickly unmix data which is both macroscopically and microscopically mixed. Subsequently, this method is then validated on synthetic and real datasets.<\/p>\n<h2>Links:<\/h2>\n<p> <a href=\"http:\/\/ieeexplore.ieee.org\/document\/6811795\/?reload=true&amp;arnumber=6811795\"><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\/dranishnikov2013unmixing.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>D. Dranishnikov, P. Gader, A. Zare, and T. Glenn, \u201cUnmixing using a combined microscopic and macroscopic mixture model with distinct endmembers,\u201d in 21st European Signal Proc. Conf. (EUSPICO), 2013. <\/code><\/pre>\n<pre><code>@InProceedings{dranishnikov2013unmixing,\nTitle = {Unmixing using a combined microscopic and macroscopic mixture model with distinct endmembers},\nAuthor = {Dranishnikov, Dmitri and Gader, Paul and Zare, Alina and Glenn, Taylor},\nBooktitle = {21st European Signal Proc. Conf. (EUSPICO)},\nYear = {2013},\nMonth = {Sept.},\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: Much work in the study of hyperspectral imagery has focused on macroscopic mixtures and unmixing via the linear mixing model. A substantially different approach seeks to model hyperspectral data non-linearly in order to accurately describe intimate or microscopic relationships of materials within the image. In this paper we present and discuss a new model [&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":[273,365,519,781],"class_list":["post-1034","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-endmember","tag-hyperspectral","tag-nonlinear-mixing","tag-unmixing"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1034","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=1034"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1034\/revisions"}],"predecessor-version":[{"id":14641,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1034\/revisions\/14641"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=1034"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=1034"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=1034"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}