{"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/band-selection-with-higher-order-multivariate","title":"Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images","arxiv_id":"1808.03513","date":"2018-08-10","proceeding":null,"authors":["Przemysław Głomb","Krzysztof Domino","Michał Romaszewski","Michał Cholewa"],"abstract":"In the small target detection problem a pattern to be located is on the order\nof magnitude less numerous than other patterns present in the dataset. This\napplies both to the case of supervised detection, where the known template is\nexpected to match in just a few areas and unsupervised anomaly detection, as\nanomalies are rare by definition. This problem is frequently related to the\nimaging applications, i.e. detection within the scene acquired by a camera. To\nmaximize available data about the scene, hyperspectral cameras are used; at\neach pixel, they record spectral data in hundreds of narrow bands.\n  The typical feature of hyperspectral imaging is that characteristic\nproperties of target materials are visible in the small number of bands, where\nlight of certain wavelength interacts with characteristic molecules. A\ntarget-independent band selection method based on statistical principles is a\nversatile tool for solving this problem in different practical applications.\n  Combination of a regular background and a rare standing out anomaly will\nproduce a distortion in the joint distribution of hyperspectral pixels. Higher\nOrder Cumulants Tensors are a natural `window' into this distribution, allowing\nto measure properties and suggest candidate bands for removal. While there have\nbeen attempts at producing band selection algorithms based on the 3 rd\ncumulant's tensor i.e. the joint skewness, the literature lacks a systematic\nanalysis of how the order of the cumulant tensor used affects effectiveness of\nband selection in detection applications. In this paper we present an analysis\nof a general algorithm for band selection based on higher order cumulants. We\ndiscuss its usability related to the observed breaking points in performance,\ndepending both on method order and the desired number of bands. Finally we\nperform experiments and evaluate these methods in a hyperspectral detection\nscenario.","url_abs":"http://arxiv.org/abs/1808.03513v1","url_pdf":"http://arxiv.org/pdf/1808.03513v1.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":"band-selection-with-higher-order-multivariate","repo_url":"https://github.com/UnofficialJuliaMirror/CumulantsFeatures.jl-89efba0d-c40c-5510-8345-5c0ed49e5930","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"band-selection-with-higher-order-multivariate","repo_url":"https://github.com/UnofficialJuliaMirrorSnapshots/CumulantsFeatures.jl-89efba0d-c40c-5510-8345-5c0ed49e5930","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"band-selection-with-higher-order-multivariate","repo_url":"https://github.com/ZKSI/CumFSel.jl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"band-selection-with-higher-order-multivariate","repo_url":"https://github.com/ZKSI/CumulantsFeatures.jl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"band-selection-with-higher-order-multivariate","repo_url":"https://github.com/iitis/CumulantsFeatures.jl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"unsupervised-anomaly-detection","task_name":"Unsupervised Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}