Résumé: Military, agricultural, and urban planning have all made extensive use of SAR/ISAR in the realm of remote sensing. SAR/ISAR images are more capable of identifying the details of the targets than optical images and can be taken in any condition. Due to the challenges associated with SAR/ISAR imaging, the lack of data causes many jobs relying on data-driven deep learning algorithms to perform less than satisfactorily. Cropping, rotation, and other procedures are examples of classic data augmentation techniques now in use, although they do not fundamentally differ from basic replication and cannot increase the model's stability and robustness. Deep generative models are used to generate SAR/ISAR images, which is a more efficient way than the conventional ones. The generation techniques are outlined and organized depending on the application fields in this review, including SAR/ISAR data augmentation (26 papers), SAR/ISAR image translation (29 papers), SAR/ISAR image enhancement (22 papers), azimuth interpolation (9 papers), and deceptive jamming (1 paper). The connected works are then summarized based on several deep generative models. 87 linked studies and 5 associated survey papers from 2017 to 2022 are compiled in this review. Finally, the summarized works are systematically analyzed. There are 27 papers using MSTAR for image generation, which is the mostly applied dataset. For evaluation, the combination of SSIM and PSNR is applied most widely (32.19%). In conclusion, this review offers fresh perspectives on the direction in which deep generative models for SAR/ISAR image generation are headed. The cutting-edge methods outlined in this paper are also available to researchers in other domains.

Langue: Anglais