Synthetic aperture radar target recognition of incomplete training datasets via Siamese network

Abstract

Synthetic aperture radar (SAR) target recognition can provide effective target category information and becomes the key part of SAR image application. Machine learning and deep learning are two main methods of target recognition. Normally, through the massive image features learning, the trained model can be used to infer the possible categories for various new target images, but the overfitting problem caused by limited data samples always makes the trained model unusable. In order to solve this case, the authors introduce a dual-input Siamese convolution neural network to the small samples oriented SAR target recognition. The training method looks like a kind of data enhancement method, but there are some differences between them. In the experiments, only 15 training samples are used to complete a three-class tank classification task. It means each category has just five samples while the number of corresponding testing data is 195. As a result, the recognition accuracy of the authors’ method outperforms the support vector machine, A-ConvNet, and 18-layers ResNet by 31, 13 and 16%, respectively. Siamese network has a good performance in small samples classification and the results prove the validity of the network.

Keywords

• image classification
• learning (artificial intelligence)
• radar target recognition
• synthetic aperture radar
• radar imaging
• radar computing
• convolutional neural nets
• feature extraction
• synthetic aperture radar target recognition
• incomplete training datasets
• effective target category information
• sar image application
• machine learning
• deep learning
• massive image features
• target images
• data samples
• dual-input siamese convolution neural network
• sar target recognition
• training method
• data enhancement method
• samples classification
• testing data