Hybrid Feature Extraction Based on PCA and CNN for Oil Rig Classification in C-Band SAR Imagery

• 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Naturresursteknik -- Fjärranalysteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Environmental Engineering -- Remote Sensing (hsv//eng)

Nyckelord

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

Publikations- och innehållstyp

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