{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T11:37:09Z","timestamp":1774525029262,"version":"3.50.1"},"reference-count":91,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T00:00:00Z","timestamp":1631577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-resolution images obtained by multispectral cameras mounted on Unmanned Aerial Vehicles (UAVs) are helping to capture the heterogeneity of the environment in images that can be discretized in categories during a classification process. Currently, there is an increasing use of supervised machine learning (ML) classifiers to retrieve accurate results using scarce datasets with samples with non-linear relationships. We compared the accuracies of two ML classifiers using a pixel and object analysis approach in six coastal wetland sites. The results show that the Random Forest (RF) performs better than K-Nearest Neighbors (KNN) algorithm in the classification of pixels and objects and the classification based on pixel analysis is slightly better than the object-based analysis. The agreement between the classifications of objects and pixels is higher in Random Forest. This is likely due to the heterogeneity of the study areas, where pixel-based classifications are most appropriate. In addition, from an ecological perspective, as these wetlands are heterogeneous, the pixel-based classification reflects a more realistic interpretation of plant community distribution.<\/jats:p>","DOI":"10.3390\/rs13183669","type":"journal-article","created":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T21:47:21Z","timestamp":1631656041000},"page":"3669","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2159-7689","authenticated-orcid":false,"given":"Ricardo","family":"Mart\u00ednez Prentice","sequence":"first","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"}]},{"given":"Miguel","family":"Villoslada Peci\u00f1a","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7391-5530","authenticated-orcid":false,"given":"Raymond D.","family":"Ward","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"},{"name":"Centre for Aquatic Environments, School of the Environment and Technology, University of Brighton, Cockcroft Building, Moulsecoomb, Brighton BN2 4GJ, UK"}]},{"given":"Thaisa F.","family":"Bergamo","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5152-8380","authenticated-orcid":false,"given":"Chris B.","family":"Joyce","sequence":"additional","affiliation":[{"name":"Centre for Aquatic Environments, School of the Environment and Technology, University of Brighton, Cockcroft Building, Moulsecoomb, Brighton BN2 4GJ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8076-7943","authenticated-orcid":false,"given":"Kalev","family":"Sepp","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1139\/A09-012","article-title":"Ground vegetation as an indicator of ecological integrity","volume":"17","author":"LaPaix","year":"2009","journal-title":"Environ. 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