{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:29:26Z","timestamp":1775838566295,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T00:00:00Z","timestamp":1620345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2003109"],"award-info":[{"award-number":["U2003109"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Laboratory of Space Utilization, Chinese Academy of Sciences","award":["No. LSU-KFJJ-2019-11"],"award-info":[{"award-number":["No. LSU-KFJJ-2019-11"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["No. XDA23090304"],"award-info":[{"award-number":["No. XDA23090304"]}]},{"name":"Youth Innovation Promotion Association of the Chinese Academy of Science","award":["Y201935"],"award-info":[{"award-number":["Y201935"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Impact craters refer to the most salient features on the moon surface. They are of huge significance for analyzing the moon topography, selecting the lunar landing site and other lunar exploration missions, etc. However, existing methods of impact crater detection have been largely implemented on the optical image data, thereby causing them to be sensitive to the sunlight. Thus, these methods can easily achieve unsatisfactory detection results. In this study, an original two-stage small crater detection method is proposed, which is sufficiently effective in addressing the sunlight effects. At the first stage of the proposed method, a semantic segmentation is conducted to detect small impact craters by fully exploiting the elevation information in the digital elevation map (DEM) data. Subsequently, at the second stage, the detection accuracy is improved under the special post-processing. As opposed to other methods based on DEM images, the proposed method, respectively, increases the new crusher percentage, recall and crusher level F1 by 4.89%, 5.42% and 0.67%.<\/jats:p>","DOI":"10.3390\/rs13091826","type":"journal-article","created":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T03:28:36Z","timestamp":1620962916000},"page":"1826","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Detection of Small Impact Craters via Semantic Segmenting Lunar Point Clouds Using Deep Learning Network"],"prefix":"10.3390","volume":"13","author":[{"given":"Yifan","family":"Hu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1799-3948","authenticated-orcid":false,"given":"Jun","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2517-7593","authenticated-orcid":false,"given":"Lupeng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0096-403X","authenticated-orcid":false,"given":"Long","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, China"}]},{"given":"Ying","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.icarus.2018.06.022","article-title":"Lunar crater identification via deep learning","volume":"317","author":"Silburt","year":"2019","journal-title":"Icarus"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/0019-1035(88)90006-1","article-title":"Crater size-frequency distributions and a revised Martian relative chronology","volume":"75","author":"Barlow","year":"1988","journal-title":"Icarus"},{"key":"ref_3","unstructured":"Rodionova, J., Dekchtyareva, K., Khramchikhin, A., Michael, G., Ajukov, S., Pugacheva, S., and Shevchenko, V. 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