Electrooptical Image Synthesis from SAR Imagery Using Generative Adversarial Networks

The utility of Synthetic Aperture Radar (SAR) imagery in remote sensing and satellite image analysis is well established, offering robustness under various weather and lighting conditions. However, SAR images, characterized by their unique structural and texture characteristics, often pose interpretability challenges for analysts accustomed to electrooptical (EO) imagery. This application compares state-of-the-art Generative Adversarial Networks (GANs) including Pix2Pix, CycleGan, S-CycleGan, and a novel dualgenerator GAN utilizing partial convolutions and a novel dual-generator architecture utilizing transformers. These models are designed to progressively refine the realism in the translated optical images, thereby enhancing the visual interpretability of SAR data. We demonstrate the efficacy of our approach through qualitative and quantitative evaluations, comparing the synthesized EO images with actual EO images in terms of visual fidelity and feature preservation. The results show significant improvements in interpretability, making SAR data more accessible for analysts familiar with EO imagery. Furthermore, we explore the potential of this technology in various applications, including environmental monitoring, urban planning, and military reconnaissance, where rapid, accurate interpretation of SAR data is crucial. Our research contributes to the field of remote sensing by bridging the gap between SAR and EO imagery, offering a novel tool for enhanced data interpretation and broader application of SAR technology in various domains. NTRODUCTION Synthetic Aperture Radar (SAR) systems are capable of creating high-resolution remote sensing images of the earths surface from satellite and aircraft. These images offer several key advantages over standard electro-optical (EO) images, most significantly, the ability to penetrate clouds and operate independently of daylight, which has led to SAR systems being deployed extensively in various fields, including environmental monitoring, natural disaster assessment, military reconnaissance, and geological mapping [1]. Figure 1 shows the benefit of a SAR image when cloud coverage is present. Despite these advantages, SAR images poses significant challenges and still has drawbacks compared to EO images, specifically regarding human interpretability.