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

Please use this identifier to cite or link to this item: https://www.repositorio.mar.mil.br/handle/ripcmb/846499
Title: Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images
Authors: Silva, Fabiano Gabriel da
Ramos, Lucas P.
Palm, Bruna G.
Machado, Renato
Keywords: Machine learning
Oil rig classification
SAR
DGPM knowledge areas: Sensoriamento remoto
Issue Date: 2022
Publisher: Remote Sens
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
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.
Access: Open access
URI: https://www.repositorio.mar.mil.br/handle/ripcmb/846499
Type: Journal article
Appears in Collections:Hidrografia e Navegação: Coleção de Artigos

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