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As a mission-critical sensor, SAR has been applied in environmental monitoring and battlefield surveillance; moreover, SAR target recognition is one of the most important applications of SAR technology. However, in practical applications, the number of samples available for training is relatively small, so the SAR target recognition can be regarded as a small sample recognition problem. One of the main directions to solve the small sample recognition problem is to realize the data augmentation. Therefore, a SAR image data augmentation method via Generative Adversarial Nets (GAN) is proposed in this paper. The method uses Wasserstein GAN with a gradient penalty (WGAN-GP) to generate new samples based on existing SAR data, which can augment the sample number in training dataset. Meanwhile, the sample selection filters are designed to extract the generated samples with high quality and specific azimuth, which can avoid the randomness of the data augmentation, and improve the quality of the newly generated training samples. The experiments based on MSTAR data show that, for three-class recognition problem, when the training sample is only 108, the proposed method can improve the recognition rate from 79% to 91.6%; and for ten-class recognition problem, when the training sample is only 360, the proposed method can improve the recognition rate from 57.48% to 79.59%. Compared with the traditional data linear generation method, the proposed method shows significant improvement on the quantity and quality of the training samples, and can effectively solve the problem of the small sample recognition.

Convolutional neural networks (CNNs) have been widely used in synthetic aperture radar (SAR) target recognition. Traditional CNNs suffer from expensive computation and high memory consumption, impeding their deployment in real-time recognition systems of SAR sensors, as these systems have low memory resources and low speed of calculation. In this paper, a micro CNN (MCNN) for real-time SAR recognition system is proposed. The proposed MCNN has only two layers, and it is compressed from a deep convolutional neural network (DCNN) with 18 layers by a novel knowledge distillation algorithm called gradual distillation. MCNN is a ternary network, and all its weights are either −1 or 1 or 0. Following a student-teacher paradigm, the DCNN is the teacher network and MCNN is its student network. The gradual distillation makes MCNN a better learning route than traditional knowledge distillation. The experiments on the MSTAR dataset show that the proposed MCNN can obtain a high recognition rate which is almost the same as the DCNN. However, compared with the DCNN, the memory footprint of the proposed MCNN is compressed 177 times, and the calculated amount is 12.8 times less, which means that the proposed MCNN can obtain better performance with the smaller network. INDEX TERMS SAR target recognition, micro convolutional neural network, knowledge distillation, model compression, ternary network.

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In this paper, a new Region-based Convolutional Neural Networks (RCNN) method is proposed for target recognition in large scene synthetic aperture radar (SAR) images. To locate and recognize the targets in SAR images, there are three steps in the traditional procedure: detection, discrimination, classification and recognition. Each step is supposed to provide optimal processing results for the next step, but this is difficult to implement in real-life applications because of speckle noise and inefficient connection among these procedures. To solve this problem, the RCNN is applied to large scene SAR target recognition, which can detect the objects while recognizing their classes based on its regression method and the sharing network structure. However, size of the input images to RCNN is limited so that the classification could be accomplished, which leads to a problem that RCNN is not able to handle the large scene SAR images directly. Thus, before the RCNN, a fast sliding method is proposed to segment the scene image into sub-images with suitable size and avoid dividing targets into different sub-images. After the RCNN, candidate regions on different slices are predicted. To locate targets on large scene SAR images from these candidate regions on small slices, the Non-maximum Suppression between Regions (NMSR) is proposed, which could find the most proper candidate region among all the overlapped regions. Experiments on 1476 × 1784 simulated MSTAR images of simple scenes and complex scenes show that the proposed method can recognize all targets with the best accuracy and fastest speed, and outperform the other methods, such as constant false alarm rate (CFAR) detector + support vector machine (SVM), Visual Attention+SVM, and Sliding-RCNN.