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Deep learning has reached excellent results in various applications of computer vision, such as image classification, segmentation or object detection. However, due to the lack of labeled data, it is not always possible to fully exploit the potential of this approach for target recognition in synthetic aperture radar (SAR) images. Indeed, most of the time, the targets are not available for a large range of aspect or depression angles. Moreover, unlike in computer vision, common data augmentation cannot be considered because of the physical mechanisms arising in SAR imaging. To overcome these difficulties, we can use simulators based on physical models. Unfortunately, these models are either too simplified to generate realistic SAR images or require too much calculation time. Moreover, even the most accurate model cannot include all physical phenomena. Thus, fine-tuning or domain adaptation methods should be implemented. Another way, considered in this paper, consists in using Generative Adversarial Networks (GAN) to generate synthetic SAR images. However, training GANs from a small database is still a challenging problem. In this contribution, to complete the missing aspect angles in the database, we explore several GANs with class and aspect angle conditions. Numerical results show that they allow to improve the performance of classifiers.
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