Simulation-assisted SAR Target Classification Based on Unsupervised Domain Adaptation and Model Interpretability Analysis

Abstract

Convolutional Neural Networks (CNNs) are widely used in optical image classification. In the case of Synthetic Aperture Radar (SAR) images, obtaining sufficient training examples for CNNs is challenging due to the difficulties in and high cost of data annotation. Meanwhile, with the advancement of SAR image simulation technology, generating a large number of simulated SAR images with annotation is not difficult. However, due to the inevitable difference between simulated and real SAR images, it is frequently difficult to directly support the real SAR image classification. As a result, this study proposes a simulation-assisted SAR target classification method based on unsupervised domain adaptation. The proposed method integrates Multi-Kernel Maximum Mean Distance (MK-MMD) with domain adversarial training to address the domain shift problem encountered during task transition from simulated to real-world SAR image classification. Furthermore, Layer-wise Relevance Propagation (LRP) and Contrastive Layer-wise Relevance Propagation (CLRP) are utilized to explore how the proposed method influences the model decision. The experimental results show that by modifying the focus areas of the model to obtain domain-invariant features for classification, the proposed method can significantly improve classification accuracy.

Keywords

• synthetic aperture radar (sar)
• target classification
• convolutional neural network (cnn)
• unsupervised domain adaptation (uda)
• interpretability