Título:	Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images
Autor(es):	Silva, Fabiano Gabriel da Ramos, Lucas P. Palm, Bruna G. Machado, Renato
Palavras-chave:	Machine learning Oil rig classification SAR
Áreas de conhecimento da DGPM:	Sensoriamento remoto
Data do documento:	2022
Editor:	Remote Sens
Citação:	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
Descrição:	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.
Tipo de Acesso:	Acesso aberto
URI:	https://www.repositorio.mar.mil.br/handle/ripcmb/846499
Tipo:	Artigo científico
Aparece nas coleções:	Hidrografia e Navegação: Coleção de Artigos

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