  Generative Adversarial Networks (GANs) have shown tremendous potential in
synthesizing a large number of realistic SAR images by learning patterns in the
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 GAN latent space
control is entirely unsupervised, allowing image processing to be conducted
without any labeled data. 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 the GAN latent
space and finding meaningful directions; (2) establishing a comprehensive SAR
image processing framework while achieving multiple image processing functions.
In the implementation of GUE, we decompose the entangled semantic directions in
the GAN latent space by training a carefully designed network. Moreover, we can
accomplish multiple SAR image processing tasks (including despeckling,
localization, auxiliary identification, and rotation editing) in a single
training process without any form of supervision. Extensive experiments
validate the effectiveness of the proposed method.
