{"id":4764,"date":"2019-08-12T08:17:19","date_gmt":"2019-08-12T13:17:19","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/?p=4764"},"modified":"2026-04-07T11:44:12","modified_gmt":"2026-04-07T15:44:12","slug":"developing-spectral-libraries-using-multiple-target-multiple-instance-adaptive-cosine-coherence-estimator","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2019\/08\/12\/developing-spectral-libraries-using-multiple-target-multiple-instance-adaptive-cosine-coherence-estimator\/","title":{"rendered":"Developing Spectral Libraries Using Multiple Target Multiple Instance Adaptive Cosine\/Coherence Estimator"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>Traditional methods of developing spectral libraries for unmixing hyperspectral images tend to require domain knowledge of the study area and the material\u2019s spectra. In this paper, we propose using the Multiple Target Multiple Instance Adaptive Cosine\/Coherence Estimator (Multi-Target MI-ACE) algorithm to develop spectral libraries that will capture the same spectral variability as traditional methods but require less processing time and domain knowledge. We compared traditional and Multi-Target MI-ACE generated spectral libraries\u2019 ability to accurately predict sub-pixel composition using Multiple Endmember Spectral Mixture Analysis (MESMA). Multi-Target MI-ACE spectral libraries maintained the same sub-pixel composition accuracy compared to traditional libraries, while significantly reducing model complexity. Additionally, the Multi-Target MI-ACE confidence values could be used to constrain MESMA model complexity and considerably reduce the number of endmember permutations needed. In summary, Multi-Target MI-ACE has been found to successfully develop spectral libraries that capture the full spectral variability compared to traditional approaches, while reducing MESMA model complexity and the need for domain knowledge.<\/p>\n<h2>Links:<\/h2>\n<p><a href=\"https:\/\/ieeexplore.ieee.org\/abstract\/document\/8920989\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-426 size-full\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/ieee.jpg\" alt=\"I triple E link of the paper\" width=\"100\" height=\"100\" \/><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre><code>S. Meerdink, J. Bocinsky, E. Wetherley, A. Zare, C. McCurley, and P. Gader, \u201cDeveloping Spectral Libraries Using Multiple Target Multiple Instance Adaptive Cosine\/Coherence Estimator,\u201d 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp 1-5, Sept. 2019.<\/code><\/pre>\n<pre><code>@Article{Meerdink2019DevelopingSpectralLibrariesMTMIACE,  \nTitle = {Developing Spectral Libraries Using Multiple Target Multiple Instance Adaptive Cosine\/Coherence Estimator}, \nAuthor = {S. Meerdink and J. Bocinsky and E. Wetherley and A. Zare and C. McCurley and P. Gader},  \nBooktitle={10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)}\nYear = {2019},  \nmonth={Sep.},  \npages={1-5},\n}<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: Traditional methods of developing spectral libraries for unmixing hyperspectral images tend to require domain knowledge of the study area and the material\u2019s spectra. In this paper, we propose using the Multiple Target Multiple Instance Adaptive Cosine\/Coherence Estimator (Multi-Target MI-ACE) algorithm to develop spectral libraries that will capture the same spectral variability as traditional methods [&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":"off","footnotes":"","_links_to":"","_links_to_target":""},"categories":[17,9,13],"tags":[53,115,151,273,365,487],"class_list":["post-4764","post","type-post","status-publish","format-standard","hentry","category-conference_paper","category-news","category-publication","tag-uncertain-imprecise-labels","tag-band-selection","tag-classification","tag-endmember","tag-hyperspectral","tag-multiple-instance"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/4764","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=4764"}],"version-history":[{"count":2,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/4764\/revisions"}],"predecessor-version":[{"id":16391,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/4764\/revisions\/16391"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=4764"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=4764"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=4764"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}