{"id":1006,"date":"2013-07-11T11:06:07","date_gmt":"2013-07-11T16:06:07","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=1006"},"modified":"2026-02-18T11:28:04","modified_gmt":"2026-02-18T16:28:04","slug":"glenn2013subpixel","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2013\/07\/11\/glenn2013subpixel\/","title":{"rendered":"Subpixel target detection in hyperspectral imagery using piece-wise convex spatial-spectral unmixing, possibilistic and fuzzy clustering, and co-registered LiDAR"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>A new algorithm for subpixel target detection in hyperspectral imagery is proposed which uses the PFCM-FLICM-PCE algorithm to model and estimate the parameters of the image background. This method uses the piece-wise convex mixing model with spatial-spectral constraints, and uses possibilistic and fuzzy clustering techniques to find the piece-wise convex regions and robustly estimate the parameters. A method for integrating the elevation measurements of a co-registered LiDAR sensor is also proposed. The performance of the proposed methods is demonstrated on a real-world dataset with emplaced detection targets.<\/p>\n<h2>Links:<\/h2>\n<p> <a href=\"\/\/doi.org\/10.1109\/IGARSS.2013.6721347\"><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\/glenn2013subpixel.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>T. Glenn, D. Dranishnikov, P. Gader, and A. Zare, \u201cSubpixel target detection in hyperspectral imagery using piece-wise convex spatial-spectral unmixing, possibilistic and fuzzy clustering, and co-registered LiDAR,\u201d in IEEE Int. Geoscience and Remote Sens. Symp.(IGARSS)), 2013, pp. 1063-1066. <\/code><\/pre>\n<pre><code>@InProceedings{glenn2013subpixel,\nTitle = {Subpixel target detection in hyperspectral imagery using piece-wise convex spatial-spectral unmixing, possibilistic and fuzzy clustering, and co-registered LiDAR},\nAuthor = {Taylor Glenn and Dmitri Dranishnikov and Paul Gader and Alina Zare},\nBooktitle = {IEEE Int. Geoscience and Remote Sens. Symp.(IGARSS))},\nYear = {2013},\nMonth = {July},\nPages = {1063-1066},\nDoi = {10.1109\/IGARSS.2013.6721347},\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: A new algorithm for subpixel target detection in hyperspectral imagery is proposed which uses the PFCM-FLICM-PCE algorithm to model and estimate the parameters of the image background. This method uses the piece-wise convex mixing model with spatial-spectral constraints, and uses possibilistic and fuzzy clustering techniques to find the piece-wise convex regions and robustly estimate [&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":[273,313,365,421,659,683,781],"class_list":["post-1006","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-endmember","tag-fusion","tag-hyperspectral","tag-lidar","tag-segmentation","tag-spatial","tag-unmixing"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1006","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=1006"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1006\/revisions"}],"predecessor-version":[{"id":14647,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1006\/revisions\/14647"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=1006"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=1006"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=1006"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}