Dual-Branch Feature Decoupling GAN with Wavelet Constraint for Azimuth-Controllable SAR Image Simulation

Synthetic aperture radar (SAR) is of great value in intelligent image interpretation. However, the acquisition of real SAR data is costly, and manual annotation heavily relies on expert experience. These factors severely restrict the development of SAR intelligent interpretation algorithms. Meanwhile, the high-frequency details of SAR images contain rich target information. Traditional generation methods cannot effectively capture these key features. To address the above issues, this paper proposes a dual-branch feature decoupling generative adversarial network (GAN) with wavelet constraint designed to achieve high-quality and parameter-controllable SAR image generation. The framework leverages discrete wavelet transform (DWT) to separate spatial structure from high-frequency details, which are independently modeled by a structure branch and a detail branch, respectively. A wavelet consistency loss function is introduced to constrain the distribution of generated and real images in high-frequency subbands, thereby enhancing the model’s capability to model scattering details. To fuse features from the two branches, a cross-attention fusion module is adopted to realize the adaptive compensation of structural features with texture details. Furthermore, to achieve joint control over the semantic attributes and azimuth of generated samples, the framework further integrates auxiliary classification and azimuth regression tasks. A multi-task learning mechanism is constructed to realize precise control over the target’s semantic category and azimuth. For the continuous variable of azimuth, an angle-aware hypernetwork transform module is introduced to perform dynamic convolution modulation on the structure branch at the feature map scale, which improves the model’s fine control capability over continuous azimuth variations. Experimental results on the MSTAR dataset demonstrate that the proposed model can significantly improve the semantic consistency and visual fidelity of the generated samples. The generated samples exhibit high statistical alignment with real data distributions, confirming the model’s effectiveness in characterizing the feature space of SAR imagery and enabling controllable SAR data simulation, thereby augmenting datasets for image interpretation tasks.