Multi-task SAR Image Processing via GAN-based Unsupervised Manipulation

TL;DR

The paper tackles the challenge of unsupervised, interpretable, multi-task SAR image processing by introducing GUE, a framework that uncouples semantic directions in StyleGAN latent spaces and trains a reconstructor to link latent edits to outcomes. GUE enables despeckling, background segmentation, rotation editing, and guided SAR target recognition in a single training run without labeled data, achieved via a decoupled, orthogonal latent direction matrix and a second-stage reconstructor. The approach yields strong despeckling performance, competitive segmentation results, and enhanced recognition through rotation semantics, demonstrating the practical value of latent space editing for SAR. These results suggest a promising label-free path toward versatile SAR image processing and interpretation, with potential for extending long-range edits as GAN inversion and latent space models improve.

Abstract

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.

Figures (17)

• Figure 1: Visualization of t-SNE van2008visualizing for latent space samples. GAN latent space contains a wealth of semantic information, and moving along semantic directions in the latent space enables editing.
• Figure 2: Left: The structure of the StyleGAN2 karras2020analyzing generator, containing three different latent spaces: the original input space denoted as $z$, the intermediate latent space represented as $w$, and the modulated latent space $w^{+}$. Right: The Image2StyleGAN abdal2019image2styleganwei2022e2style algorithm, corresponding to the optimized reconstruction outcomes of the three latent spaces, demonstrates deformations in the $z$ and $w$ space reconstruction results, while the $w^{+}$ space reconstruction most faithfully reverts the original image.
• Figure 3: Compared with optical images, SAR images are more sensitive to rotation transformation. Standard rotation semantics include (1) azimuth rotation (first column); (2) pitch angle rotation (second column); (3) attitude transformation (third column).
• Figure 4: Illustrative Instances of SAR Rotation: These instances encompass the azimuth rotation of the 2S1, the pitch rotation of the BRDM-2, and the attitude transformation of the ZSU-23-4 (which encompasses imaging results depicting two distinct attitudes characterized by varying azimuth angles).
• Figure 5: Method overview: Given a pretrained generator $G$, find possible interpretable directions in the GANs$\prime$ latent space. Given a set of latent vectors satisfying a specific distribution karras2019style, the displacement operator $z+\alpha A e_n$ is obtained by the direction selection operator and displacement distance operator after defined transformation. The image pair obtained after inputting the original hidden vector $z$ and displacement vector $z^{\prime}$ into $G$ restores the direction index $n$ and displacement distance $\alpha$ through the reconstructor $R$. During the optimization process, $A$ and $G$ are optimized simultaneously, and each column of $A$ is automatically decoupled.