Training of synthetic aperture radar (SAR) target detection and recognition methods based on deep learning heavily relies on a large amount of data. As one of the significant approaches to address the scarcity of SAR data, SAR image intelligent generation methods have witnessed rapid development. However, these methods often require many data samples for learning and are prone to deviating from the physical scattering characteristics. To address these issues, this letter proposes a two-stage SAR image generation method based on attribute feature decoupling within a generative adversarial network (GAN) architecture. In the first stage, the original SAR target image undergoes feature extraction and reconstruction, yielding generated images highly similar to real images. The attribute features decoupled during this process correlate with the scattering characteristics of SAR target, providing guiding information for generating target images in the second stage. In the second stage, by applying perturbations to specific dimensions of the decoupled features, we can reconstruct target images with altered attributes, achieving diverse data augmentation. Multitask discrimination based on pixel intensity, authenticity, and feature distance differences enhances the quality of generated images across multiple levels. The decoupled representation-driven generation paradigm simplifies the network's mapping learning task through task decomposition, diminishing the dependency on the volume of data. The experimental results demonstrate that the generated images possess higher quality and superior application performance, with an improvement of 5.23% in recognition accuracy.
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