{"_buckets": {"deposit": "3205c665-42ce-40f2-a054-2524ff825a9d"}, "_deposit": {"id": "32167", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "32167"}, "status": "published"}, "_oai": {"id": "oai:niigata-u.repo.nii.ac.jp:00032167"}, "item_5_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2019-03", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "3", "bibliographicPageEnd": "80", "bibliographicPageStart": "73", "bibliographicVolumeNumber": "8", "bibliographic_titles": [{"bibliographic_title": "IEICE Communications Express"}, {"bibliographic_title": "IEICE Communications Express", "bibliographic_titleLang": "en"}]}]}, "item_5_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "Polarimetric similarity is a parameter for measuring the similarity between two scattering mechanisms. In this paper, we propose a novel model-based target classification technique using a compensated polarimetric similarity parameter between two coherency matrices. In general, the ensemble average coherency matrix elements have magnitude imbalance, thus the contribution degree to the polarimetric similarity differs for each element. We illustrate how to compensate the contribution degree, and then the proposed method is tested on L-band fully polarimetric ALOS-2/PALSAR-2 data sets by using 4 theoretical scattering models (surface scattering, double-bounce scattering, volume scattering, and 22.5\uff9f oriented dihedral scattering). The classification results show that the new compensation scheme serves to better classification.", "subitem_description_type": "Abstract"}]}, "item_5_publisher_7": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "The Institute of Electronics, Information and Communication Engineers"}]}, "item_5_relation_14": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "info:doi/10.1587/comex.2018XBL0152", "subitem_relation_type_select": "DOI"}}]}, "item_5_relation_31": {"attribute_name": "\u7570\u7248\u3067\u3042\u308b", "attribute_value_mlt": [{"subitem_relation_type": "isVersionOf", "subitem_relation_type_id": {"subitem_relation_type_id_text": "https://www.jstage.jst.go.jp/", "subitem_relation_type_select": "URI"}}, {"subitem_relation_type": "isVersionOf", "subitem_relation_type_id": {"subitem_relation_type_id_text": "http://www.ieice.org/jpn/trans_online/", "subitem_relation_type_select": "URI"}}]}, "item_5_rights_15": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "Copyright(C)2019 IEICE"}, {"subitem_rights": "\u672c\u6587\u30c7\u30fc\u30bf\u306f\u5b66\u5354\u4f1a\u306e\u8a31\u8afe\u306b\u57fa\u3065\u304dJ-Stage\u304b\u3089\u8907\u88fd\u3057\u305f\u3082\u306e\u3067\u3042\u308b"}]}, "item_5_select_19": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_5_source_id_9": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "21870136", "subitem_source_identifier_type": "ISSN"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Umemura, Maito"}], "nameIdentifiers": [{"nameIdentifier": "175338", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Yamaguchi, Yoshio"}], "nameIdentifiers": [{"nameIdentifier": "19", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Yamada, Hiroyoshi"}], "nameIdentifiers": [{"nameIdentifier": "50", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2019-09-10"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "8(3)_73-80.pdf", "filesize": [{"value": "4.1 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 4099999.9999999995, "url": {"label": "8(3)_73-80.pdf", "url": "https://niigata-u.repo.nii.ac.jp/record/32167/files/8(3)_73-80.pdf"}, "version_id": "bae4ba6d-2217-420c-81e8-33733ba2c8e5"}]}, "item_keyword": {"attribute_name": "\u30ad\u30fc\u30ef\u30fc\u30c9", "attribute_value_mlt": [{"subitem_subject": "polarimetric synthetic aperture radar", "subitem_subject_scheme": "Other"}, {"subitem_subject": "polarimetric similarity", "subitem_subject_scheme": "Other"}, {"subitem_subject": "coherency matrix", "subitem_subject_scheme": "Other"}, {"subitem_subject": "PolSAR terrain classification", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Model-based target classification using polarimetric similarity with coherency matrix elements", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "Model-based target classification using polarimetric similarity with coherency matrix elements"}]}, "item_type_id": "5", "owner": "1", "path": ["453/454", "423/424/425"], "permalink_uri": "http://hdl.handle.net/10191/51113", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2019-06-06"}, "publish_date": "2019-06-06", "publish_status": "0", "recid": "32167", "relation": {}, "relation_version_is_last": true, "title": ["Model-based target classification using polarimetric similarity with coherency matrix elements"], "weko_shared_id": 2}