The availability of high-quality and ample synthetic aperture radar (SAR) image datasets is crucial for understanding and recognizing target characteristics. However, in practical applications, the limited availability of SAR target images significantly impedes the advancement of SAR interpretation methodologies. In this study, we introduce a Generative Adversarial Network (GAN)-based approach designed to manipulate the target azimuth angle with few samples, thereby generating high-quality target images with adjustable angle ranges. The proposed method consists of three modules: a generative fusion local module conditioned on image features, a controllable angle generation module based on sparse representation, and an angle discrimination module based on scattering point extraction. Consequently, the generative modules fuse semantically aligned features from different images to produce diverse SAR samples, whereas the angle synthesis module constructs target images within a specified angle range. The discriminative module comprises a similarity discriminator to distinguish between authentic and synthetic images to ensure the image quality, and an angle discriminator to verify that generated images conform to the specified range of the azimuth angle. Combining these modules, the proposed methodology is capable of generating azimuth angle-controllable target images using only a limited number of support samples. The effectiveness of the proposed method is not only verified through various quality metrics, but also examined through the enhanced distinguishability of target recognition methods. In our experiments, we achieved SAR image generation within a given angle range on two datasets. In terms of generated image quality, our method has significant advantages over other methods in metrics such as FID and SSIM. Specifically, the FID was reduced by up to 0.37, and the SSIM was increased by up to 0.46. In the target recognition experiments, after augmenting the data, the accuracy improved by 6.16% and 3.29% under two different pitch angles, respectively. This demonstrates that our method has great advantages in the SAR image generation task, and the research content is of great value.

Synthetic Aperture Radar (SAR) enables the acquisition of high-resolution imagery even under severe meteorological and illumination conditions. Its utility is evident across a spectrum of applications, particularly in automatic target recognition (ATR). Since SAR samples are often scarce in practical ATR applications, there is an urgent need to develop sample-efficient augmentation techniques to augment the SAR images. However, most of the existing generative approaches require an excessive amount of training samples for effective modeling of the SAR imaging characteristics. Additionally, they show limitations in augmenting the interesting target samples while maintaining image recognizability. In this study, we introduce an innovative single-sample image generation approach tailored to SAR data augmentation. To closely approximate the target distribution across both the spatial layout and local texture, a multi-level Generative Adversarial Network (GAN) architecture is constructed. It comprises three distinct GANs that independently model the structural, semantic, and texture patterns. Furthermore, we introduce multiple constraints including prior-regularized noise sampling and perceptual loss optimization to enhance the fidelity and stability of the generation process. Comparative evaluations against the state-of-the-art generative methods demonstrate the superior performance of the proposed method in terms of generation diversity, recognizability, and stability. In particular, its advantages over the baseline method are up to 0.2 and 0.22 in the SIFID and SSIM, respectively. It also exhibits stronger robustness in the generation of images across varying spatial sizes.