{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multiple-instance-hyperspectral-target","title":"Multiple Instance Hyperspectral Target Characterization","arxiv_id":"1606.06354","date":"2016-06-20","proceeding":null,"authors":["Alina Zare","Changzhe Jiao","Taylor Glenn"],"abstract":"In this paper, two methods for multiple instance target characterization,\nMI-SMF and MI-ACE, are presented. MI-SMF and MI-ACE estimate a discriminative\ntarget signature from imprecisely-labeled and mixed training data. In many\napplications, such as sub-pixel target detection in remotely-sensed\nhyperspectral imagery, accurate pixel-level labels on training data is often\nunavailable and infeasible to obtain. Furthermore, since sub-pixel targets are\nsmaller in size than the resolution of a single pixel, training data is\ncomprised only of mixed data points (in which target training points are\nmixtures of responses from both target and non-target classes). Results show\nimproved, consistent performance over existing multiple instance concept\nlearning methods on several hyperspectral sub-pixel target detection problems.","url_abs":"http://arxiv.org/abs/1606.06354v3","url_pdf":"http://arxiv.org/pdf/1606.06354v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multiple-instance-hyperspectral-target","repo_url":"https://github.com/GatorSense/FUMI","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}