{"created":"2023-05-15T08:43:50.872251+00:00","id":8968,"links":{},"metadata":{"_buckets":{"deposit":"6e0e6d3d-4710-4d0b-ad60-da2eaf0fafe4"},"_deposit":{"created_by":13,"id":"8968","owners":[13],"pid":{"revision_id":0,"type":"depid","value":"8968"},"status":"published"},"_oai":{"id":"oai:uec.repo.nii.ac.jp:00008968","sets":["6"]},"author_link":["24441","24442","24440"],"item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2017-07-26","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"10","bibliographicPageStart":"3","bibliographicVolumeNumber":"248","bibliographic_titles":[{},{"bibliographic_title":"Neurocomputing","bibliographic_titleLang":"en"}]}]},"item_10001_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Polarimetric satellite-borne synthetic aperture radar (PolSAR) is expected to provide land usage information globally and precisely. In this paper, we propose a unsupervised double-stage learning land state classification system using a self-organizing map (SOM) that utilizes ensemble variation vectors. We find that the Poincare sphere parameters representing the polarization state of scattered wave have specific features of the land state, in particular, in their ensemble variation rather than spatial variation. Experiments demonstrate that the proposed PolSAR double-stage SOM system generate new classes appropriately, resulting in successful fine land classification and/or appropriate new class generation.","subitem_description_type":"Abstract"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Elsevier"}]},"item_10001_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isVersionOf","subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1016/j.neucom.2016.11.072","subitem_relation_type_select":"DOI"}}]},"item_10001_relation_17":{"attribute_name":"関連サイト","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1016/j.neucom.2016.11.072","subitem_relation_type_select":"DOI"}}]},"item_10001_rights_15":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"© 2017 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/"}]},"item_10001_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"09252312","subitem_source_identifier_type":"ISSN"}]},"item_10001_version_type_20":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_ab4af688f83e57aa","subitem_version_type":"AM"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Takizawa, Yuto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shang, Fang","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hirose, Akira","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2019-07-26"}],"displaytype":"detail","filename":"6_neucom_takizawa.pdf","filesize":[{"value":"12.7 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"6_neucom_takizawa","url":"https://uec.repo.nii.ac.jp/record/8968/files/6_neucom_takizawa.pdf"},"version_id":"057a1c00-bed4-43fa-b849-2f451bd12f58"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Synthetic aperture radar","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Polarimetry","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Self-organizing map","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Geoscience big data","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"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":"Adaptive land classification and new class generation by unsupervised double-stage learning in Poincare sphere space for polarimetric synthetic aperture radars","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Adaptive land classification and new class generation by unsupervised double-stage learning in Poincare sphere space for polarimetric synthetic aperture radars","subitem_title_language":"en"}]},"item_type_id":"10001","owner":"13","path":["6"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-02-12"},"publish_date":"2019-02-12","publish_status":"0","recid":"8968","relation_version_is_last":true,"title":["Adaptive land classification and new class generation by unsupervised double-stage learning in Poincare sphere space for polarimetric synthetic aperture radars"],"weko_creator_id":"13","weko_shared_id":-1},"updated":"2023-05-15T10:07:18.720045+00:00"}