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Nevertheless, existing retrieval techniques, encompassing physics-based and pixel-based statistical methodologies such as support vector regression (SVR), band ratio, and Kriging regression, exhibit limitations stemming from the intricate water reflectance process and the under-exploitation of the spatial component inherent in SDB. To surmount these obstacles, we introduce employment of deep convolutional networks (DCNs) for SDB in this study. We assembled multiple scenes utilizing networks with varying scale emphasis and an assortment of satellite datasets characterized by distinct spatial and spectral resolutions. Our findings reveal that these deep learning models yield high-caliber bathymetry outcomes, with nonlinear normalization further mitigating residuals in shallow water regions and substantially enhancing retrieval performance. A comparative analysis with the prevalent SVR technique substantiates the efficacy of the proposed methodology.<\/jats:p>","DOI":"10.3390\/rs15174247","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T10:09:49Z","timestamp":1693390189000},"page":"4247","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Improving Shallow Water Bathymetry Inversion through Nonlinear Transformation and Deep Convolutional Neural Networks"],"prefix":"10.3390","volume":"15","author":[{"given":"Shuting","family":"Sun","sequence":"first","affiliation":[{"name":"China Waterborne Transport Research Institute, Beijing 100088, China"}]},{"given":"Yifu","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences (Wuhan), Wuhan 430079, China"},{"name":"Hubei Luojia Laboratory, Wuhan University, Wuhan 430072, China"},{"name":"Donghai Laboratory, Zhejiang University, Zhoushan 316036, China"}]},{"given":"Lin","family":"Mu","sequence":"additional","affiliation":[{"name":"College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China"},{"name":"College of Marine Science and Technology, China University of Geosciences (CUG), Wuhan 430079, China"}]},{"given":"Yuan","family":"Le","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences (Wuhan), Wuhan 430079, China"}]},{"given":"Huihui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100045, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/S0378-3839(98)00022-2","article-title":"Coastal engineering applications of high-resolution lidar bathymetry","volume":"35","author":"Irish","year":"1998","journal-title":"Coast. 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