Hybrid feature extraction based on PCA and CNN for oil rig classification in C-Band SAR imagery
| Autor: | da Silva, Fabiano G., Ramos, Lucas P., Palm, Bruna, Alves, Dimas I., Pettersson, Mats, Machado, Renato |
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| Rok vydání: | 2022 |
| Předmět: |
Image classification
Feature extraction techniques Decision trees Principal component analysis Features extraction Logistic regression Extraction Synthetic aperture radar Feature Extraction Machine Learning Remote Sensing C-bands Fjärranalysteknik Machine-learning Synthetic Aperture Radar Imagery PCA Classification (of information) C-Band Oil-rigs Radar imaging Nearest neighbor search Support vector regression Hybrid-feature extraction Sentinel-1 Target Classification Convolutional neural networks CNN SAR |
| Zdroj: | Artificial Intelligence and Machine Learning in Defense Applications IV. |
| DOI: | 10.1117/12.2636274 |
| Popis: | 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. open access |
| Databáze: | OpenAIRE |
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