{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T07:57:03Z","timestamp":1768463823636,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"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":["41906168"],"award-info":[{"award-number":["41906168"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["1908085QD161"],"award-info":[{"award-number":["1908085QD161"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University Natural Science Research Key Project of Anhui Province","award":["KJ2019A0024"],"award-info":[{"award-number":["KJ2019A0024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As widely applicated in many underwater research fields, conventional side-scan sonars require the sonar height to be at the seabed for geocoding seabed images. However, many interference factors, including compensation with unknown gains, suspended matters, etc., would bring difficulties in bottom detection. Existing methods need manual parameter setups or to use postprocessing methods, which limits automatic and real-time processing in complex situations. To solve this problem, a one-dimensional U-Net (1D-UNet) model for sea bottom detection of side-scan data and the bottom detection and tracking method based on 1D-UNet are proposed in this work. First, the basic theory of sonar bottom detection and the interference factors is introduced, which indicates that deep learning of the bottom is a feasible solution. Then, a 1D-UNet model for detecting the sea bottom position from the side-scan backscatter strength sequences is proposed, and the structure and implementation of this model are illustrated in detail. Finally, the bottom detection and tracking algorithms of a single ping and continuous pings are presented on the basis of the proposed model. The measured side-scan sonar data in Meizhou Bay and Bayuquan District were selected in the experiments to verify the model and methods. The 1D-UNet model was first trained and applied with the side-scan data in Meizhou Bay. The training and validation accuracies were 99.92% and 99.77%, respectively, and the sea bottom detection accuracy of the training survey line was 99.88%. The 1D-UNet model showed good robustness to the interference factors of bottom detection and fully real-time performance in comparison with other methods. Moreover, the trained 1D-UNet model is used to process the data in the Bayuquan District for proving model generality. The proposed 1D-UNet model for bottom detection has been proven effective for side-scan sonar data and also has great potentials in wider applications on other types of sonars.<\/jats:p>","DOI":"10.3390\/rs13051024","type":"journal-article","created":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T12:12:18Z","timestamp":1615205538000},"page":"1024","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Bottom Detection from Backscatter Data of Conventional Side Scan Sonars through 1D-UNet"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9434-1388","authenticated-orcid":false,"given":"Jun","family":"Yan","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Anhui Province Engineering Laboratory for Mine Ecological Remediation, Hefei 230601, China"}]},{"given":"Junxia","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Anhui Jianzhu University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3796-8405","authenticated-orcid":false,"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"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.rse.2006.11.029","article-title":"Time-lapse side-scan sonar imaging of bleached coral reefs: A case study from the Seychelles","volume":"108","author":"Collier","year":"2007","journal-title":"Remote Sens. 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