{"id":1172,"date":"2015-05-11T15:29:05","date_gmt":"2015-05-11T20:29:05","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=1172"},"modified":"2026-02-18T11:28:01","modified_gmt":"2026-02-18T16:28:01","slug":"zare2015functions","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2015\/05\/11\/zare2015functions\/","title":{"rendered":"Functions of multiple instances for sub-pixel target characterization in hyperspectral imagery"},"content":{"rendered":"

Abstract:<\/h2>\n

In this paper, the Multi-target Extended Function of Multiple Instances (Multi-target eFUMI) method is developed and described. The method is capable of learning multiple target spectral signatures from weakly- and inaccurately-labeled hyperspectral imagery. Multi-target eFUMI is a generalization of the Function of Multiple Instances approach (FUMI). The FUMI approach differs significantly from standard Multiple Instance Learning (MIL) approach in that it assumes each data is a function of target and non-target “concepts.” In this paper, data points which are convex combinations of multiple target and several non-target “concepts” are considered. Moreover, it allows both “proportion-level” and “bag-level” uncertainties in training data. Training data needs only binary labels indicating whether some spatial area contains or does not contain some proportion of target; the specific target proportions for the training data are not needed. Multi-target eFUMI learns the target and non-target concepts, the number of non-target concepts, and the proportions of all the concepts for each data point. After learning the target concepts using the binary “bag-level” labeled training data, target detection can be performed on test data. Results for sub-pixel target detection on simulated and real airborne hyperspectral data are shown.<\/p>\n

Links:<\/h2>\n

\"\u201cSPIE\"<\/a> \"PDF\"<\/a> \"PDF\"<\/a>\"PDF\"<\/a><\/p>\n

Citation:<\/h2>\n
A. Zare and C. Jiao, \u201cFunctions of multiple instances for sub-pixel target characterization in hyperspectral imagery,\u201d in Proc. SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, 2015. <\/code><\/pre>\n
@InProceedings{zare2015functions,\nTitle = {Functions of multiple instances for sub-pixel target characterization in hyperspectral imagery},\nAuthor = {Zare, Alina and Jiao, Changzhe},\nBooktitle = {Proc. SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI},\nYear = {2015},\nMonth = {May},\nNumber = {947212},\nVolume = {9472},\nDoi = {10.1117\/12.2176889},\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"
Abstract: In this paper, the Multi-target Extended Function of Multiple Instances (Multi-target eFUMI) method is developed and described. The method is capable of learning multiple target spectral signatures from weakly- and inaccurately-labeled hyperspectral imagery. Multi-target eFUMI is a generalization of the Function of Multiple Instances approach (FUMI). The FUMI approach differs significantly from standard Multiple […]<\/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":[53,273,365,487,681,733,781],"class_list":["post-1172","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-uncertain-imprecise-labels","tag-endmember","tag-hyperspectral","tag-multiple-instance","tag-sparsity-promotion","tag-target-detection","tag-unmixing"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1172","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=1172"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1172\/revisions"}],"predecessor-version":[{"id":14613,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1172\/revisions\/14613"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=1172"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=1172"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=1172"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}