Abstract
Inspired by their tremendous success in optical image detection and classification, convolutional neural networks (CNNs) have recently been used in synthetic aperture radar automatic target recognition (SAR-ATR). Although CNN-based methods can achieve excellent recognition performance, it is difficult to collect a large number of real SAR images available for training. In this paper, we introduce simulated SAR data to alleviate the problem of insufficient training data. To address domain shift and task transfer problems caused by differences between simulated and real data, we propose a model that integrates meta-learning and adversarial domain adaptation. We use sufficient simulated data and a few real data to pre-train the model. After fine-tuning, the pre-trained model can quickly adapt to new tasks in real data. Extensive experimental results obtained in the real SAR dataset demonstrate that our model effectively solves the cross-domain and cross-task transfer problem. Compared with conventional SAR-ATR methods, the proposed model can achieve better recognition performance with a small amount of training data.
Highlights
Synthetic aperture radar (SAR) is an active sensor mounted on moving platforms such as aircraft, satellites, and spaceships
We use a small amount of real SAR data to fine-tune the model and test it on the remaining real SAR data
Compared with other traditional synthetic aperture radar automatic target recognition (SAR-Automatic target recognition (ATR)) methods, our model achieves state-of-the-art results
Summary
Synthetic aperture radar (SAR) is an active sensor mounted on moving platforms such as aircraft, satellites, and spaceships. SAR provides two-dimensional high-resolution images by receiving the electromagnetic echoes of targets. Benefiting from its unique imaging mechanism, SAR can operate day and night, independent of weather conditions, and has specific surface penetration capability. The SAR system has unique advantages in many applications, ranging from disaster monitoring and resource exploration to military inspection, and it plays an unreplaceable role in both military and civilian fields. Automatic target recognition (ATR) is an essential topic in the field of SAR application research. According to different implementation methods, classic ATR methods can be classified into feature-based and model-based approaches
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