Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

Silva, Fabiano Gabriel da; Ramos, Lucas P.; Palm, Bruna G.; Machado, Renato

Repositório Institucional da Produção Científica da Marinha do Brasil (RI-MB)

Use este identificador para citar ou linkar para este item: https://www.repositorio.mar.mil.br/handle/ripcmb/846499

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Campo DC	Valor	Idioma
dc.contributor.author	Silva, Fabiano Gabriel da	-
dc.contributor.author	Ramos, Lucas P.	-
dc.contributor.author	Palm, Bruna G.	-
dc.contributor.author	Machado, Renato	-
dc.date.accessioned	2023-12-19T18:05:44Z	-
dc.date.available	2023-12-19T18:05:44Z	-
dc.date.issued	2022	-
dc.identifier.citation	da Silva, F.G.; Ramos, L.P.; Palm, B.G.; Machado, R. Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images. Remote Sens. 2022, 14, 2966. https://doi.org/10.3390/rs14132966	pt_BR
dc.identifier.uri	https://www.repositorio.mar.mil.br/handle/ripcmb/846499	-
dc.description	This article aims at performing maritime target classification in SAR images using machine learning (ML) and deep learning (DL) techniques. 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–Wallis 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.	pt_BR
dc.language.iso	en	pt_BR
dc.publisher	Remote Sens	pt_BR
dc.rights	openAccess	pt_BR
dc.subject	Machine learning	pt_BR
dc.subject	Oil rig classification	pt_BR
dc.subject	SAR	pt_BR
dc.title	Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images	pt_BR
dc.type	journalArticle	pt_BR
dc.identifier.doi	https://doi.org/10.3390/rs14132966	-
dc.subject.dgpm	Sensoriamento remoto	pt_BR
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