{"created":"2021-03-01T07:23:03.308899+00:00","id":43447,"links":{},"metadata":{"_buckets":{"deposit":"c903bc57-d5ad-4784-bebc-4a749753cae0"},"_deposit":{"id":"43447","owners":[],"pid":{"revision_id":0,"type":"depid","value":"43447"},"status":"published"},"_oai":{"id":"oai:tsukuba.repo.nii.ac.jp:00043447","sets":["152:1954","3:62:5591:6100"]},"item_5_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2017-11","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"11","bibliographicPageEnd":"5461","bibliographicPageStart":"5447","bibliographicVolumeNumber":"26","bibliographic_titles":[{"bibliographic_title":"IEEE transactions on image processing"}]}]},"item_5_creator_3":{"attribute_name":"著者別名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"福井, 和広"}],"nameIdentifiers":[{},{},{}]}]},"item_5_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Hyperspectral images (HSIs) possess non-negative properties for both hyperspectral signatures and abundance coefficients, which can be naturally modeled using cone-based representation. However, in hyperspectral target detection, cone-based methods are barely studied. In this paper, we propose a new regularized cone-based representation approach to hyperspectral target detection, as well as its two working models by incorporating into the cone representation l2-norm and l1-norm regularizations, respectively. We call the new approach the matched shrunken cone detector (MSCD). Also important, we provide principled derivations of the proposed MSCD from the Bayesian perspective: we show that MSCD can be derived by assuming a multivariate half-Gaussian distribution or a multivariate half-Laplace distribution as the prior distribution of the coefficients of the models. In the experimental studies, we compare the proposed MSCD with the subspace methods and the sparse representation-based methods for HSI target detection. Two real hyperspectral data sets are used for evaluating the detection performances on sub-pixel targets and full-pixel targets, respectively. Results show that the proposed MSCD can outperform other methods in both cases, demonstrating the competitiveness of the regularized cone-based representation.","subitem_description_type":"Abstract"}]},"item_5_publisher_27":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"IEEE"}]},"item_5_relation_10":{"attribute_name":"PubMed番号","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"28816671","subitem_relation_type_select":"PMID"}}]},"item_5_relation_11":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1109/TIP.2017.2740621","subitem_relation_type_select":"DOI"}}]},"item_5_rights_12":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/"}]},"item_5_select_15":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_select_item":"publisher"}]},"item_5_source_id_7":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1057-7149","subitem_source_identifier_type":"ISSN"}]},"item_5_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA10821122","subitem_source_identifier_type":"NCID"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Wang, Ziyu"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Zhu, Rui"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Fukui, Kazuhiro"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Xue, Jing-Hao"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2017-09-26"}],"displaytype":"detail","filename":"IEEETIP_26-11.pdf","filesize":[{"value":"2.8 MB"}],"format":"application/pdf","licensetype":"license_6","mimetype":"application/pdf","url":{"label":"IEEETIP_26-11","url":"https://tsukuba.repo.nii.ac.jp/record/43447/files/IEEETIP_26-11.pdf"},"version_id":"05ad26d0-2b89-4c01-ae65-62331c546114"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Matched Shrunken Cone Detector (MSCD): Bayesian Derivations and Case Studies for Hyperspectral Target Detection","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Matched Shrunken Cone Detector (MSCD): Bayesian Derivations and Case Studies for Hyperspectral Target Detection"}]},"item_type_id":"5","owner":"1","path":["1954","6100"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-09-26"},"publish_date":"2017-09-26","publish_status":"0","recid":"43447","relation_version_is_last":true,"title":["Matched Shrunken Cone Detector (MSCD): Bayesian Derivations and Case Studies for Hyperspectral Target Detection"],"weko_creator_id":"1","weko_shared_id":5},"updated":"2022-04-27T09:14:31.278862+00:00"}