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Sökning: id:"swepub:oai:DiVA.org:bth-23505" > Assessment of Machi...

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

da Silva, Fabiano G. (författare)
Aeronautics Institute of Technology (ITA), BRA
Ramos, Lucas P. (författare)
Aeronautics Institute of Technology (ITA), BRA
Palm, Bruna (författare)
Blekinge Tekniska Högskola,Institutionen för matematik och naturvetenskap
visa fler...
Machado, Renato (författare)
Aeronautics Institute of Technology (ITA), BRA
visa färre...
 (creator_code:org_t)
2022-06-21
2022
Engelska.
Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 14:13
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Naturresursteknik -- Fjärranalysteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Environmental Engineering -- Remote Sensing (hsv//eng)

Nyckelord

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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