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applied in acquiring seabed topography and underwater sonar images. However, when interference occurs in the water column, traditional bottom detection methods may fail, resulting in discontinuities in the bathymetry and distortion in the sonar images. To solve this problem, we propose an anti-interference bottom detection method based on deep learning models. First, the variation differences of backscatter strengths at different incidence angles and the failure conditions of traditional methods were analyzed. Second, the details of our deep learning models are explained. And these models were trained using samples in the specular reflection, scatter reflection, and high-incidence angle regions, respectively. Third, the bottom detection procedures of the along-track and across-track water column data using the trained models are provided. In the experiments, multibeam data with strong interferences in the water column were selected. The bottom detection results of the along-track water column data at incidence angles of 0\u00b0, 35\u00b0, and 60\u00b0 and the across-track ping data validated the effectiveness of our method. By comparison, our method acquired the correct bottom position when the traditional methods had inaccurate or even no detection results. Our method can be used to supplement existing methods and effectively improve bathymetry robustness under interference conditions.<\/jats:p>","DOI":"10.3390\/rs16030530","type":"journal-article","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T07:49:47Z","timestamp":1706600987000},"page":"530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Anti-Interference Bottom Detection Method of Multibeam Echosounders Based on Deep Learning Models"],"prefix":"10.3390","volume":"16","author":[{"given":"Junxia","family":"Meng","sequence":"first","affiliation":[{"name":"College of Civil Engineering, Anhui Jianzhu University, Hefei 230601, China"}]},{"given":"Jun","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Engineering Center for Geographic Information of Anhui Province, Anhui University, Hefei 230601, China"}]},{"given":"Qinghe","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Deep Coal Mining Response and Disaster Prevention and Control, Anhui University of Science and Technology, Huainan 232001, China"},{"name":"School of Civil Engineering and Architecture, Anhui University of Science and Technology, Huainan 232001, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1007\/BF00313877","article-title":"Shallow-water imaging multibeam sonars: A new tool for investigating seafloor processes in the coastal zone and on the continental shelf","volume":"18","author":"Mayer","year":"1996","journal-title":"Mar. 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