{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T10:04:57Z","timestamp":1778493897135,"version":"3.51.4"},"reference-count":157,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a comprehensive overview of current deep-learning methods for automatic object classification of underwater sonar data for shoreline surveillance, concentrating mostly on the classification of vessels from passive sonar data and the identification of objects of interest from active sonar (such as minelike objects, human figures or debris of wrecked ships). Not only is the contribution of this work to provide a systematic description of the state of the art of this field, but also to identify five main ingredients in its current development: the application of deep-learning methods using convolutional layers alone; deep-learning methods that apply biologically inspired feature-extraction filters as a preprocessing step; classification of data from frequency and time\u2013frequency analysis; methods using machine learning to extract features from original signals; and transfer learning methods. This paper also describes some of the most important datasets cited in the literature and discusses data-augmentation techniques. The latter are used for coping with the scarcity of annotated sonar datasets from real maritime missions.<\/jats:p>","DOI":"10.3390\/s22062181","type":"journal-article","created":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T01:40:23Z","timestamp":1646962823000},"page":"2181","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8205-1562","authenticated-orcid":false,"given":"Lucas C. F.","family":"Domingos","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Centro Universit\u00e1rio FEI, Sao Bernardo do Campo 09850-901, SP, Brazil"},{"name":"Department of Computer Vision, Instituto de Pesquisas Eldorado, Campinas 13083-898, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8484-0354","authenticated-orcid":false,"given":"Paulo E.","family":"Santos","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Centro Universit\u00e1rio FEI, Sao Bernardo do Campo 09850-901, SP, Brazil"},{"name":"Centre for Defence Engineering Research and Training, College of Science and Engineering, Flinders University, Tonsley, SA 5042, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6192-8193","authenticated-orcid":false,"given":"Phillip S. M.","family":"Skelton","sequence":"additional","affiliation":[{"name":"Centre for Defence Engineering Research and Training, College of Science and Engineering, Flinders University, Tonsley, SA 5042, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0270-3538","authenticated-orcid":false,"given":"Russell S. 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