{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:20:32Z","timestamp":1775143232559,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T00:00:00Z","timestamp":1631059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41576107"],"award-info":[{"award-number":["41576107"]}]},{"name":"National Natural Science Foundation of China","award":["41376109"],"award-info":[{"award-number":["41376109"]}]},{"name":"National Key R&amp;D Program of China","award":["2016YFB0501703"],"award-info":[{"award-number":["2016YFB0501703"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Horizon picking from sub-bottom profiler (SBP) images has great significance in marine shallow strata studies. However, the mainstream automatic picking methods cannot handle multiples well, and there is a need to set a group of parameters manually. Considering the constant increase in the amount of SBP data and the high efficiency of deep learning (DL), we proposed a physicals-combined DL method to pick the horizons from SBP images. We adopted the DeeplabV3+ net to extract the horizons and multiples from SBP images. We generated a training dataset from the Jiaozhou Bay survey (Shandong, China) and the Zhujiang estuary survey (Guangzhou, China) to increase the applicability of the trained model. After the DL processing, we proposed a simulated Radon transform method to eliminate the surface-related multiples from the prediction by combining the designed pseudo-Radon transform and correlation analysis. We verified the proposed method using actual data (not involved in the training dataset) from Jiaozhou Bay and Zhujiang estuary. The positions of picked horizons are accurate, and multiples are suppressed.<\/jats:p>","DOI":"10.3390\/rs13183565","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T10:12:03Z","timestamp":1631095923000},"page":"3565","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Horizon Picking from SBP Images Using Physicals-Combined Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Jie","family":"Feng","sequence":"first","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"},{"name":"Institute of Marine Science and Technology, Wuhan University, Wuhan 430079, China"}]},{"given":"Jianhu","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"},{"name":"Institute of Marine Science and Technology, Wuhan University, Wuhan 430079, China"}]},{"given":"Gen","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"},{"name":"Institute of Marine Science and Technology, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1208-7778","authenticated-orcid":false,"given":"Shaobo","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"},{"name":"Institute of Marine Science and Technology, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s00367-015-0410-x","article-title":"Evidence of extensive carbonate mounds and sublacustrine channels in shallow waters of Lake Van, eastern Turkey, based on high-resolution chirp subbottom profiler and multibeam echosounder data","volume":"35","author":"Cukur","year":"2015","journal-title":"Geo-Mar. 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