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Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images
Aeronautics Institute of Technology (ITA), BRA.
Aeronautics Institute of Technology (ITA), BRA.
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0003-0423-9927
Aeronautics Institute of Technology (ITA), BRA.
2022 (English)In: Remote Sensing, E-ISSN 2072-4292, Vol. 14, no 13, article id 2966Article in journal (Refereed) Published
Abstract [en]

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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
MDPI , 2022. Vol. 14, no 13, article id 2966
Keywords [en]
classification algorithms, deep learning, machine learning, oil rig classification, SAR, ship classification, Adaptive boosting, Classification (of information), Convolutional neural networks, Decision trees, Image classification, Learning systems, Nearest neighbor search, Radar imaging, Ships, Support vector machines, C-bands, Campos Basin, Classification algorithm, Machine-learning, Oil-rigs, Synthetic aperture radar images, Target Classification, Synthetic aperture radar
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Remote Sensing
Identifiers
URN: urn:nbn:se:bth-23505DOI: 10.3390/rs14132966ISI: 000825692700001Scopus ID: 2-s2.0-85132981546OAI: oai:DiVA.org:bth-23505DiVA, id: diva2:1686807
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open access

Available from: 2022-08-11 Created: 2022-08-11 Last updated: 2023-08-28Bibliographically approved

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Palm, Bruna

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