{"id":812,"date":"2011-07-11T07:54:22","date_gmt":"2011-07-11T12:54:22","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=812"},"modified":"2026-02-18T11:28:06","modified_gmt":"2026-02-18T16:28:06","slug":"zare2011spatial","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2011\/07\/11\/zare2011spatial\/","title":{"rendered":"Spatial-spectral unmixing using fuzzy local information"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>Hyperspectral unmixing estimates the proportions of materials represented within a spectral signature. The over whelming majority of hyperspectral unmixing algorithms are based entirely on the spectral signatures of each individual pixel and do not incorporate the spatial information found in a hyperspectral data cube. In this work, a spectral unmixing algorithm, the Local Information Proportion estimation (LIP) algorithm, is presented. The proposed LIP algorithm incorporates spatial information while determining the proportions of materials found within a spectral signature. Spatial information is incorporated through the addition of a spatial term that regularizes proportion value estimates based on the weighted proportion values of neighboring pixels. Results are shown in the AVIRIS Indian Pines hyperspectral data set.<\/p>\n<h2>Links:<\/h2>\n<p> <a href=\"http:\/\/doi.org\/10.1109\/IGARSS.2011.6049398\"><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\/zare2011spatial.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>A. Zare, \u201cSpatial-spectral unmixing using fuzzy local information,\u201d in IEEE Int. Geoscience and Remote Sens. Symposium (IGARSS), 2011, pp. 1139-1142.<\/code><\/pre>\n<pre><code>@InProceedings{zare2011spatial,\nTitle = {Spatial-spectral unmixing using fuzzy local information},\nAuthor = {Zare, Alina},\nBooktitle = {IEEE Int. Geoscience and Remote Sens. Symposium (IGARSS)},\nYear = {2011},\nMonth = {July},\nPages = {1139-1142},\nDoi = {10.1109\/IGARSS.2011.6049398},\n}<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: Hyperspectral unmixing estimates the proportions of materials represented within a spectral signature. The over whelming majority of hyperspectral unmixing algorithms are based entirely on the spectral signatures of each individual pixel and do not incorporate the spatial information found in a hyperspectral data cube. In this work, a spectral unmixing algorithm, the Local Information [&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":[273,365,683,781],"class_list":["post-812","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-endmember","tag-hyperspectral","tag-spatial","tag-unmixing"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/812","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=812"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/812\/revisions"}],"predecessor-version":[{"id":14685,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/812\/revisions\/14685"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=812"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=812"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=812"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}