A Two-Stage SAR Image Generation Algorithm Based on GAN with Reinforced Constraint Filtering and Compensation Techniques

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 11; p. 1963
• Main Authors: Liu, Ming, Wang, Hongchen, Chen, Shichao, Tao, Mingliang, Wei, Jingbiao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Accuracy; Algorithms; Artificial intelligence; Automatic target recognition; automatic target recognition (ATR); Categories; Comparative analysis; Compensation; Data augmentation; Datasets; Deep learning; Filtration; Game theory; generative adversarial network (GAN); Generative adversarial networks; image generation; Image processing; Image quality; Image resolution; Methods; Neural networks; Synthetic aperture radar; synthetic aperture radar (SAR); Target acquisition; Technology application; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16111963

Generative adversarial network (GAN) can generate diverse and high-resolution images for data augmentation. However, when GAN is applied to the synthetic aperture radar (SAR) dataset, the generated categories are not of the same quality. The unrealistic category will affect the performance of the subsequent automatic target recognition (ATR). To overcome the problem, we propose a reinforced constraint filtering with compensation afterwards GAN (RCFCA-GAN) algorithm to generate SAR images. The proposed algorithm includes two stages. We focus on improving the quality of easily generated categories in Stage 1. Then, we record the categories that are hard to generate and compensate by using traditional augmentation methods in Stage 2. Thus, the overall quality of the generated images is improved. We conduct experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset. Recognition accuracy and Fréchet inception distance (FID) acquired by the proposed algorithm indicate its effectiveness.