{"created":"2023-06-20T14:35:33.243421+00:00","id":1778,"links":{},"metadata":{"_buckets":{"deposit":"4d39a0e1-cd7a-47fe-b272-3068af5f46d5"},"_deposit":{"created_by":1,"id":"1778","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"1778"},"status":"published"},"_oai":{"id":"oai:jaxa.repo.nii.ac.jp:00001778","sets":["1398:1399:1400","1887:1893","9:10:13:17"]},"author_link":["475672","475664","475669","475663","475665","475668","475671","475667","475666","475670"],"item_3_alternative_title_2":{"attribute_name":"その他のタイトル（英）","attribute_value_mlt":[{"subitem_alternative_title":"Identification of moon central peak craters by machine learning using Kaguya DEM"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2019-03-08","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"10","bibliographicPageStart":"1","bibliographicVolumeNumber":"JAXA-RR-18-008","bibliographic_titles":[{"bibliographic_title":"宇宙航空研究開発機構研究開発報告: 宇宙科学情報解析論文誌: 第8号"},{"bibliographic_title":"JAXA Research and Development Report: Journal of Space Science Informatics Japan: Volume 8","bibliographic_titleLang":"en"}]}]},"item_3_description_16":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"月には大小多数のクレーターが存在している. その中には, 「中央丘」という特殊な構造物を有するクレーター（以下「中央丘クレーター」と呼称）が存在している. この中央丘には, 月面表面に表出していながらも, 月地殻内部の物質が露出しているという重要な特徴がある. すなわち, 中央丘表面の探査によって, 周囲の内部地殻の物質を推定することが可能となるのである. この内部地殻の分析により, クレーター及び中央丘の成因の推定や, 過去の月面の表層環境や地殻変動の過程が推定できることが期待される. しかし現在, 中央丘クレーターの探査が盛んに行われてはいない. その要因として, 中央丘の存在確認が専門家の画像目視によるものであるが故, 中央丘クレーターとして知られているクレーターが数少ないことが挙げられる. これを解決するため, 中央丘クレーターの発見手法を自動化することで, 中央丘クレーターの場所, 大きさ等を網羅した一覧（本研究では中央丘クレーターカタログと呼称）を作成し, 今後の月面探査において調査対象の候補となりうる中央丘を大幅に増加させることが求められる. よって本研究では, 中央丘クレーターカタログの作成を最終目標とし, その為の中央丘クレーターの自動発見手法を提案する. 本研究においては, JAXAの月周囲衛星「かぐや(SELENE)」の観測にもたらされた月面の数値標高モデル（DEM）を用い, 機械学習による中央丘クレーターの識別を行い, それが中央丘クレーターカタログを作成できるほどの精度を有しているか検証する. 具体的には, まず回転ピクセルスワッピング法というDEMデータからの高速クレーター抽出手法を用いて各クレーターのDEMデータを抽出, それらにラベル付けを行った後, CNNによる中央丘クレーターの識別を試る. 結果として, 中央丘クレーターカタログを作成できるほどの高精度な識別モデルを得ることはできなかったが, 中央丘クレーター識別においてCNNが有効な手法であることは確認できた.","subitem_description_type":"Abstract"}]},"item_3_description_17":{"attribute_name":"抄録（英）","attribute_value_mlt":[{"subitem_description":"There are many craters in the moon. Among them are craters having a special structure called 'central peak' (hereinafter referred to as 'the central-peak crater'). This central peak has an important characteristic that substances inside the moon crust are exposed on the moon surface. Therefore, by measuring the surface of the central peak, it is possible to estimate the material of the surrounding inner crust. By analyzing the inner crust, it is expected that estimation of the cause of craters and central peaks, the process of the environment of the moon surface, and crustal deformation of the past. However, except for some famous craters, the investigation has not progressed much. The reason for this is that the discovery of the central peak is based on visual observation of images by experts, so there are few known the central-peak craters. In order to solve this problem, it is necessary to automate the discovery method of the central-peak crater and prepare a catalog that records the position and size of central peaks, thereby greatly increasing the prospecting point candidate of the central-peak crater. Therefore, in this research, the final goal is to create a catalog of the central-peak crater, and for that purpose we propose an automatic discovery method of the central-peak crater. In this research, we use Digital Elevation Model (DEM) of the lunar surface observed by JAXA's lunar orbit satellite 'KAGUYA' to identify the central-peak crater by machine learning and verify its accuracy. Specifically, we first extract craters using a high-speed crater automatic extraction method called 'Rotating Pixel Swapping Method for DEM', label them, and then try to identify the central-peak crater by CNN. As a result, it was impossible to obtain a highly accurate discrimination model that could create the catalog of the central-peak craters, but we could confirm the possibility that CNN is an effective method in the central-peak crater identification.","subitem_description_type":"Other"}]},"item_3_description_18":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"形態: カラー図版あり","subitem_description_type":"Other"}]},"item_3_description_19":{"attribute_name":"内容記述（英）","attribute_value_mlt":[{"subitem_description":"Physical characteristics: Original contains color illustrations","subitem_description_type":"Other"}]},"item_3_description_32":{"attribute_name":"資料番号","attribute_value_mlt":[{"subitem_description":"資料番号: AA1830035001","subitem_description_type":"Other"}]},"item_3_description_33":{"attribute_name":"レポート番号","attribute_value_mlt":[{"subitem_description":"レポート番号: JAXA-RR-18-008","subitem_description_type":"Other"}]},"item_3_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20637/JAXA-RR-18-008/0001","subitem_identifier_reg_type":"JaLC"}]},"item_3_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"宇宙航空研究開発機構(JAXA)"}]},"item_3_publisher_9":{"attribute_name":"出版者（英）","attribute_value_mlt":[{"subitem_publisher":"Japan Aerospace Exploration Agency (JAXA)"}]},"item_3_source_id_22":{"attribute_name":"ISSNONLINE","attribute_value_mlt":[{"subitem_source_identifier":"2433-2216","subitem_source_identifier_type":"ISSN"}]},"item_3_text_6":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"首都大学東京"},{"subitem_text_value":"宇宙航空研究開発機構宇宙科学研究所(JAXA)(ISAS)"},{"subitem_text_value":"首都大学東京"},{"subitem_text_value":"岡山理科大学"},{"subitem_text_value":"首都大学東京"}]},"item_3_text_7":{"attribute_name":"著者所属（英）","attribute_value_mlt":[{"subitem_text_language":"en","subitem_text_value":"Tokyo Metropolitan University"},{"subitem_text_language":"en","subitem_text_value":"Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA)(ISAS)"},{"subitem_text_language":"en","subitem_text_value":"Tokyo Metropolitan University"},{"subitem_text_language":"en","subitem_text_value":"Okayama University of Science"},{"subitem_text_language":"en","subitem_text_value":"Tokyo Metropolitan University"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"原, 聡志"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山本, 幸生"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"荒木, 徹也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"廣田, 雅春"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"石川, 博"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hara, Satoshi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yamamoto, Yukio","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Araki, Tetsuya","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hirota, Masaharu","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ishikawa, Hiroshi","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":"2020-01-31"}],"displaytype":"detail","filename":"AA1830035001.pdf","filesize":[{"value":"1.6 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"AA1830035001.pdf","url":"https://jaxa.repo.nii.ac.jp/record/1778/files/AA1830035001.pdf"},"version_id":"15cf6bb5-c51c-4f06-b356-8760adb6f6b9"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Moon Central Peak","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Machine Learning","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Neural Network","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"technical report","resourceuri":"http://purl.org/coar/resource_type/c_18gh"}]},"item_title":"かぐやDEMを用いた, 機械学習による中央丘クレーター識別","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"かぐやDEMを用いた, 機械学習による中央丘クレーター識別"}]},"item_type_id":"3","owner":"1","path":["17","1400","1893"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-03-08"},"publish_date":"2019-03-08","publish_status":"0","recid":"1778","relation_version_is_last":true,"title":["かぐやDEMを用いた, 機械学習による中央丘クレーター識別"],"weko_creator_id":"1","weko_shared_id":1},"updated":"2023-06-20T20:22:09.329578+00:00"}