Multi-task SAR image processing via GAN-based unsupervised manipulation

Generative Adversarial Networks (GANs) have shown tremendous potential in synthesizing realistic SAR images by learning patterns from data distribution. Some GANs can achieve image editing by introducing latent codes, demonstrating significant promise in SAR image processing. Compared to traditional SAR image processing methods, editing based on latent space is entirely unsupervised, allowing image processing to be conducted without any label. Additionally, the information extracted from the data is more interpretable. This paper proposes a novel SAR image processing framework called GAN-based Unsupervised Editing (GUE), aiming to address the following two issues: (1) disentangling semantic directions in GANs’ latent space and finding meaningful directions; (2) establishing a comprehensive SAR image processing framework. In the implementation of GUE, we decompose the entangled semantic directions in GANs’ latent space by training a carefully designed network. Moreover, it allows us to accomplish multiple SAR image processing tasks (including despeckling, auxiliary identification, and rotation editing) in a single training process without any form of supervision. Extensive experiments validate the effectiveness of our method.