Abstract
For the problem of reliable decision in synthetic aperture radar (SAR) target recognition, a method based on updated classifiers is proposed. The convolutional neural network (CNN) and support vector machine (SVM) are used as basic classifiers to classify samples with unknown target labels. The two decisions are fused and the reliability of the fused decision is evaluated. The classified test samples with high reliabilities are added to the original training samples to update the classifiers. The updated classifiers have stronger classification abilities and the fused result of the two classifiers can obtain a more reliable decision. The proposed method is tested and verified based on the moving and stationary target acquisition and recognition (MSTAR) dataset. The experimental results verify the effectiveness and robustness of the proposed method.
Highlights
High-resolution synthetic aperture radar (SAR) can provide strong support for earth observation
Used SAR image features mainly describe target geometry [2,3,4,5,6,7], electromagnetic scattering characteristics [8,9,10], or distributions of pixel values by projection or transformation [11,12,13,14,15,16,17,18]. e classifier learns a reliable decision-making surface based on a large number of training samples and classifies the test samples. e classifiers commonly used for SAR target recognition include nearest neighbor (NN) [11], support vector machine (SVM) [19,20,21,22], and sparse representationbased classification (SRC) [23,24,25,26,27]
Compared with the SVM + SRC method, this paper introduces convolutional neural network (CNN) with stronger classification performance
Summary
High-resolution synthetic aperture radar (SAR) can provide strong support for earth observation. Feature extraction and classifier design are two important steps in the SAR target recognition methods. E former obtains high discriminative features through the analysis of SAR images, thereby improving the overall accuracy and efficiency of subsequent classification. The deep learning algorithms have been widely applied and verified in various research fields, and a large number of SAR target methods based on deep learning models have emerged. It is difficult to train a reliable deep learning classification model, which brings obstacles to the application of related methods. In [42], multisource image data (such as optical images and electromagnetic simulation data) were processed through transfer learning to assist in training neural networks suitable for SAR target recognition
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