Hybrid feature extraction based on PCA and CNN for oil rig classification in C-Band SAR imagery

Fabiano G. da Silva; Lucas P. Ramos; Bruna G. Palm; Dimas I. Alves; Mats I. Pettersson; Renato Machado

doi:10.1117/12.2636274

28 October 2022 Hybrid feature extraction based on PCA and CNN for oil rig classification in C-Band SAR imagery

Fabiano G. da Silva, Lucas P. Ramos, Bruna G. Palm, Dimas I. Alves, Mats I. Pettersson, Renato Machado

Author Affiliations +

Proceedings Volume 12276, Artificial Intelligence and Machine Learning in Defense Applications IV; 122760G (2022) https://doi.org/10.1117/12.2636274
Event: SPIE Security + Defence, 2022, Berlin, Germany

Abstract

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.

Conference Presentation

Citation Download Citation

Fabiano G. da Silva, Lucas P. Ramos, Bruna G. Palm, Dimas I. Alves, Mats I. Pettersson, and Renato Machado "Hybrid feature extraction based on PCA and CNN for oil rig classification in C-Band SAR imagery", Proc. SPIE 12276, Artificial Intelligence and Machine Learning in Defense Applications IV, 122760G (28 October 2022); https://doi.org/10.1117/12.2636274

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