{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T14:28:19Z","timestamp":1775917699032,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:00:00Z","timestamp":1689724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014510","name":"European Maritime and Fisheries Fund and the Danish Fisheries Agency","doi-asserted-by":"publisher","award":["33113-B-19-141"],"award-info":[{"award-number":["33113-B-19-141"]}],"id":[{"id":"10.13039\/100014510","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Knowledge about the spatial distribution of seagrasses is essential for coastal conservation efforts. Imagery obtained from unoccupied aerial systems (UAS) has the potential to provide such knowledge. Classifier choice and hyperparameter settings are, however, often based on time-consuming trial-and-error procedures. The presented study has therefore investigated the performance of five machine learning algorithms, i.e., Bayes, Decision Trees (DT), Random Trees (RT), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) when used for the object-based classification of submerged seagrasses from UAS-derived imagery. The influence of hyperparameter tuning and training sample size on the classification accuracy was tested on images obtained from different altitudes during different environmental conditions. The Bayes classifier performed well (94% OA) on images obtained during favorable environmental conditions. The DT and RT classifier performed better on low-altitude images (93% and 94% OA, respectively). The kNN classifier was outperformed on all occasions, while still producing OA between 89% and 95% in five out of eight scenarios. The SVM classifier was most sensitive to hyperparameter tuning with OAs ranging between 18% and 97%; however, it achieved the highest OAs most often. The findings of this study will help to choose the appropriate classifier and optimize related hyperparameter settings.<\/jats:p>","DOI":"10.3390\/rs15143600","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T21:21:46Z","timestamp":1689801706000},"page":"3600","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Comparative Assessment of Five Machine Learning Algorithms for Supervised Object-Based Classification of Submerged Seagrass Beds Using High-Resolution UAS Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0829-3771","authenticated-orcid":false,"given":"Aris","family":"Thomasberger","sequence":"first","affiliation":[{"name":"Section for Coastal Ecology, National Institute of Aquatic Resources, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark"}]},{"given":"Mette M\u00f8ller","family":"Nielsen","sequence":"additional","affiliation":[{"name":"Section for Coastal Ecology, National Institute of Aquatic Resources, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark"}]},{"given":"Mogens Rene","family":"Flindt","sequence":"additional","affiliation":[{"name":"Department of Biology, University of Southern Denmark, 5230 Odense, Denmark"}]},{"given":"Satish","family":"Pawar","sequence":"additional","affiliation":[{"name":"Section for Coastal Ecology, National Institute of Aquatic Resources, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8624-4801","authenticated-orcid":false,"given":"Niels","family":"Svane","sequence":"additional","affiliation":[{"name":"Department of Biology, University of Southern Denmark, 5230 Odense, Denmark"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/S1146-609X(99)00142-3","article-title":"Nutrient cycling and plant dynamics in estuaries: A brief review","volume":"20","author":"Flindt","year":"1999","journal-title":"Acta Oecol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.aquabot.2005.07.007","article-title":"Seagrass recovery in the Delmarva Coastal Bays, USA","volume":"84","author":"Orth","year":"2006","journal-title":"Aquat. 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