{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T11:37:13Z","timestamp":1774525033521,"version":"3.50.1"},"reference-count":118,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T00:00:00Z","timestamp":1660348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000192","name":"United States Department of Commerce\u2014National Oceanic and Atmospheric Administration (NOAA)","doi-asserted-by":"publisher","award":["NA18NOS400198"],"award-info":[{"award-number":["NA18NOS400198"]}],"id":[{"id":"10.13039\/100000192","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80\u201385.27% overall accuracy) and the DeepLabV3 (75.20\u201383.50% overall accuracy) deep learning techniques outperformed the SVM (60.50\u201371.10% overall accuracy) and the RF (57.40\u201371.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models.<\/jats:p>","DOI":"10.3390\/rs14163937","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T23:44:03Z","timestamp":1660607043000},"page":"3937","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Deep and Machine Learning Image Classification of Coastal Wetlands Using Unpiloted Aircraft System Multispectral Images and Lidar Datasets"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7898-8387","authenticated-orcid":false,"given":"Ali","family":"Gonzalez-Perez","sequence":"first","affiliation":[{"name":"School of Forest Fisheries and Geomatics Sciences, University of Florida, 1745 McCarty Drive, P.O. Box 110410, Gainesville, FL 32611, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6182-4017","authenticated-orcid":false,"given":"Amr","family":"Abd-Elrahman","sequence":"additional","affiliation":[{"name":"School of Forest Fisheries and Geomatics Sciences, University of Florida, 1745 McCarty Drive, P.O. Box 110410, Gainesville, FL 32611, USA"},{"name":"Gulf Coast Research and Education Center, 14625 CR 672, Wimauma, FL 33598, USA"}]},{"given":"Benjamin","family":"Wilkinson","sequence":"additional","affiliation":[{"name":"School of Forest Fisheries and Geomatics Sciences, University of Florida, 1745 McCarty Drive, P.O. Box 110410, Gainesville, FL 32611, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8585-2143","authenticated-orcid":false,"given":"Daniel J.","family":"Johnson","sequence":"additional","affiliation":[{"name":"School of Forest Fisheries and Geomatics Sciences, University of Florida, 1745 McCarty Drive, P.O. Box 110410, Gainesville, FL 32611, USA"}]},{"given":"Raymond R.","family":"Carthy","sequence":"additional","affiliation":[{"name":"U.S. Geological Survey, Florida Cooperative Fish & Wildlife Research Unit, P.O. Box 110485, Gainesville, FL 32611, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mendelssohn, I.A., Byrnes, M.R., Kneib, R.T., and Vittor, B.A. (2017). Coastal Habitats of the Gulf of Mexico. Habitats and Biota of the Gulf of Mexico: Before the Deepwater Horizon Oil Spill, Springer.","DOI":"10.1007\/978-1-4939-3447-8_6"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e12645","DOI":"10.1111\/conl.12645","article-title":"Are coastal habitats important nurseries? A meta-analysis","volume":"12","author":"Lefcheck","year":"2019","journal-title":"Conserv. Lett."},{"key":"ref_3","unstructured":"Florida Department of Environmental Protection (2022, August 03). 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