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Hybrid Feature Extraction Based on PCA and CNN for Oil Rig Classification in C-Band SAR Imagery
Aeronautics Institute of Technology, BRA.
Aeronautics Institute of Technology, BRA.
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0003-0423-9927
Aeronautics Institute of Technology, BRA.
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2022 (English)In: Proceedings of SPIE - The International Society for Optical Engineering / [ed] Dijk J., SPIE - International Society for Optical Engineering, 2022, article id 122760GConference paper, Published paper (Refereed)
Abstract [en]

Feature extraction techniques play an essential role in classifying and recognizing targets in synthetic aperture radar (SAR) images. This article proposes a hybrid feature extraction technique based on convolutional neural networks and principal component analysis. The proposed method is used to extract features of oil rigs and ships in C-band synthetic aperture radar polarimetric images obtained with the Sentinel-1 satellite system. The extracted features are used as input in the logistic regression (LR), support vector machine (SVM), random forest (RF), naive Bayes (NB), decision tree (DT), and k-nearest-neighbors (kNN) classification algorithms. Furthermore, the statistical tests of Kruskal-Wallis and Dunn were considered to show that the proposed extraction algorithm has a significant impact on the performance of the classifiers. © 2022 SPIE.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2022. article id 122760G
Series
Proceedings of SPIE, the International Society for Optical Engineering, ISSN 0277-786X, E-ISSN 1996-756X ; 12276
Keywords [en]
C-Band, CNN, Feature Extraction, Machine Learning, PCA, SAR, Sentinel-1, Target Classification, Classification (of information), Convolutional neural networks, Decision trees, Extraction, Image classification, Logistic regression, Nearest neighbor search, Principal component analysis, Radar imaging, Remote sensing, Support vector regression, C-bands, Feature extraction techniques, Features extraction, Hybrid-feature extraction, Machine-learning, Oil-rigs, Synthetic Aperture Radar Imagery, Synthetic aperture radar
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:bth-24195DOI: 10.1117/12.2636274ISI: 000906230300013Scopus ID: 2-s2.0-85145216918OAI: oai:DiVA.org:bth-24195DiVA, id: diva2:1726238
Conference
Artificial Intelligence and Machine Learning in Defense Applications IV 2022, Berlin, 6 September through 7 September 2022
Note

open access

Available from: 2023-01-12 Created: 2023-01-12 Last updated: 2023-02-16Bibliographically approved

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fulltext(1260 kB)170 downloads
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Palm, BrunaPettersson, Mats

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