{"id":1406,"date":"2016-09-13T11:11:42","date_gmt":"2016-09-13T16:11:42","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=1406"},"modified":"2026-02-18T11:29:08","modified_gmt":"2026-02-18T16:29:08","slug":"sheng2016hyperspectral","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2016\/09\/13\/sheng2016hyperspectral\/","title":{"rendered":"Hyperspectral Unmixing with Endmember Variability Using Partial Membership Latent Dirichlet Allocation"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>The application of Partial Membership Latent Dirichlet Allocation (PM-LDA) for hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA provides a model for a hyperspectral image analysis that accounts for spectral variability and incorporates spatial information through the use of superpixel-based &#8221;documents&#8221;. In our application of PM-LDA, we employ the Normal Compositional Model in which endmembers are represented as Normal distributions to account for spectral variability and proportion vectors are modeled as random variables governed by a Dirichlet distribution. The use of the Dirichlet distribution enforces positivity and sum-to-one constraints on the proportion values. Algorithm results on real hyperspectral data indicate that PM-LDA produces endmember distributions that represent the ground truth classes and their associated variability.<\/p>\n<h2>Links:<\/h2>\n<p> <a href=\"http:\/\/ieeexplore.ieee.org\/document\/7953348\/\"><img decoding=\"async\" border=\"2\" alt=\"\u201cIeee\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/ieee.jpg\" height=\"50\"><\/a> <a href=\"http:\/\/arxiv.org\/abs\/1609.03500\"><img decoding=\"async\" border=\"2\" alt=\"\u201cArXiv\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/arxiv.png\" height=\"50\"><\/a> <a href=\"https:\/\/github.com\/GatorSense\/Publications\/blob\/master\/zou2016HYPERSPECTRAL.pdf\"><img decoding=\"async\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/pdflogo-e1482256801729.png\" alt=\"PDF\" height=\"50\" border=\"2\" \/><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre><code>S. Zou and A. Zare, \u201cHyperspectral Unmixing with Endmember Variability Using Partial Membership Latent Dirichlet Allocation.\u201d Proc. IEEE Intl. Conf. Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, 2017.<\/code><\/pre>\n<pre><code>@InProceedings{sheng2017hyperspectral,\nTitle = {Hyperspectral Unmixing with Endmember Variability Using Partial Membership Latent Dirichlet Allocation},\nAuthor = {Zou, Sheng and Zare, Alina},\nBooktitle = {IEEE Intl. Conf. on Acoustics, Speech and Signal Processing}\nYear = {2017},\nMonth = {Mar.},\nPages = {6200-6204},\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: The application of Partial Membership Latent Dirichlet Allocation (PM-LDA) for hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA provides a model for a hyperspectral image analysis that accounts for spectral variability and incorporates spatial information through the use of superpixel-based &#8221;documents&#8221;. In our application of PM-LDA, we employ the Normal Compositional Model in [&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":[53,273,275,365,659,683,717,781],"class_list":["post-1406","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-uncertain-imprecise-labels","tag-endmember","tag-endmember-variability","tag-hyperspectral","tag-segmentation","tag-spatial","tag-superpixel","tag-unmixing"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1406","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=1406"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1406\/revisions"}],"predecessor-version":[{"id":15017,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1406\/revisions\/15017"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=1406"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=1406"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=1406"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}