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In particular, the targets of interest are oil platforms and ships located in the Campos Basin, Brazil. Two convolutional neural networks (CNNs), VGG-16 and VGG-19, were used for attribute extraction. The logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), naive Bayes (NB), neural networks (NET), and AdaBoost (ADBST) schemes were considered for classification. The target classification methods were evaluated using polarimetric images obtained from the C-band synthetic aperture radar (SAR) system Sentinel-1. Classifiers are assessed by the accuracy indicator. The LR, SVM, NET, and stacking results indicate better performance, with accuracy ranging from 84.1% to 85.5%. The Kruskal\u2013Wallis test shows a significant difference with the tested classifier, indicating that some classifiers present different accuracy results. The optimizations provide results with more significant accuracy gains, making them competitive with those shown in the literature. There is no exact combination of methods for SAR image classification that will always guarantee the best accuracy. The optimizations performed in this article were for the specific data set of the Campos Basin, and results may change depending on the data set format and the number of images.<\/jats:p>","DOI":"10.3390\/rs14132966","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T04:12:01Z","timestamp":1655871121000},"page":"2966","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4266-895X","authenticated-orcid":false,"given":"Fabiano G.","family":"da Silva","sequence":"first","affiliation":[{"name":"Department of Telecommunications, Aeronautics Institute of Technology (ITA), S\u00e3o Jos\u00e9 dos Campos 12228-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9267-6718","authenticated-orcid":false,"given":"Lucas P.","family":"Ramos","sequence":"additional","affiliation":[{"name":"Department of Telecommunications, Aeronautics Institute of Technology (ITA), S\u00e3o Jos\u00e9 dos Campos 12228-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0423-9927","authenticated-orcid":false,"given":"Bruna G.","family":"Palm","sequence":"additional","affiliation":[{"name":"Department of Telecommunications, Aeronautics Institute of Technology (ITA), S\u00e3o Jos\u00e9 dos Campos 12228-900, Brazil"},{"name":"Department of Mathematics and Natural Sciences, Blekinge Institute of Technology (BTH), 371 79 Karlskrona, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0856-5391","authenticated-orcid":false,"given":"Renato","family":"Machado","sequence":"additional","affiliation":[{"name":"Department of Telecommunications, Aeronautics Institute of Technology (ITA), S\u00e3o Jos\u00e9 dos Campos 12228-900, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"de Oliveira Soares, M., da Cruz Lotufo, T.M., Vieira, L.M., Salani, S., Hadju, E., Matthews-Cascon, H., Le\u00e3o, Z.M., Kenji, R., and de Kikuchi, P. 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