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  • da Silva, Fabiano G., et al. (author)
  • Hybrid Feature Extraction Based on PCA and CNN for Oil Rig Classification in C-Band SAR Imagery
  • 2022
  • In: Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE - International Society for Optical Engineering.
  • Conference paper (peer-reviewed)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. © 2022 SPIE.
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  • Result 1-1 of 1
Type of publication
conference paper (1)
Type of content
peer-reviewed (1)
Author/Editor
Machado, Renato (1)
Alves, Dimas I (1)
Palm, Bruna (1)
Pettersson, Mats, 19 ... (1)
Ramos, Lucas P. (1)
da Silva, Fabiano G. (1)
University
Blekinge Institute of Technology (1)
Language
English (1)
Research subject (UKÄ/SCB)
Engineering and Technology (1)
Year
2022 (1)

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