{"id":1586,"date":"2017-01-10T09:54:09","date_gmt":"2017-01-10T14:54:09","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=1586"},"modified":"2026-02-18T11:29:05","modified_gmt":"2026-02-18T16:29:05","slug":"sun2017map","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2017\/01\/10\/sun2017map\/","title":{"rendered":"Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>A map-guided superpixel segmentation method for hyperspectral imagery is developed and introduced. The proposed approach develops a hyperspectral-appropriate version of the SLIC superpixel segmentation algorithm, leverages map information to guide segmentation, and incorporates the semi-supervised Partial Membership Latent Dirichlet Allocation (sPM-LDA) to obtain a final superpixel segmentation. The proposed method is applied to two real hyperspectral data sets and quantitative cluster validity metrics indicate that the proposed approach outperforms existing hyperspectral superpixel segmentation methods.<\/p>\n<h2>Links:<\/h2>\n<p><a href=\"https:\/\/arxiv.org\/abs\/1701.01745\"><img decoding=\"async\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/arxiv.png\" alt=\"\u201cArXiv\" height=\"50\" border=\"2\" \/><\/a><a href=\"https:\/\/github.com\/GatorSense\/Publications\/blob\/master\/sun2017MAP-GUIDED.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><a href=\"https:\/\/ieeexplore.ieee.org\/document\/8127815\/\"><img decoding=\"async\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/ieee.jpg\" alt=\"\u201cIeee\" height=\"50\" border=\"2\" \/><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre><code>H. Sun and A. Zare, \u201cMap-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps\u201d Proc. IEEE Intl. Geosci. Remote Sens. Symp. (IGARSS), Fort Worth, TX, 2017. <\/code><\/pre>\n<pre><code>@InProceedings{sun2017map,\nauthor = {Sun, H. and Zare, A.},\ntitle = {Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps},\nbooktitle = {Proc. IEEE Intl. Geosci. Remote Sens. Symp.},\nyear = {2017}\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: A map-guided superpixel segmentation method for hyperspectral imagery is developed and introduced. The proposed approach develops a hyperspectral-appropriate version of the SLIC superpixel segmentation algorithm, leverages map information to guide segmentation, and incorporates the semi-supervised Partial Membership Latent Dirichlet Allocation (sPM-LDA) to obtain a final superpixel segmentation. The proposed method is applied to two [&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":[53,157,159,273,275,365,417,659,683,717,781],"class_list":["post-1586","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-uncertain-imprecise-labels","tag-cluster-validity","tag-clustering","tag-endmember","tag-endmember-variability","tag-hyperspectral","tag-latent-dirichlet-allocation","tag-segmentation","tag-spatial","tag-superpixel","tag-unmixing"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1586","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=1586"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1586\/revisions"}],"predecessor-version":[{"id":14999,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1586\/revisions\/14999"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=1586"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=1586"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=1586"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}