用生成模型把雷达图转成可见光图，让专家更容易看懂。
Electrooptical Image Synthesis from SAR Imagery Using Generative Adversarial Networks
- 用GAN模型将SAR图像转换为类可见光图像，提升可读性。
- 新双生成器架构结合Transformer与部分卷积，效果优于现有方法。
- 适合遥感、城市规划、军事侦察等需要快速判读的场景。
合成孔径雷达（SAR）在遥感与卫星图像分析中应用广泛，能在各种天气和光照条件下提供稳定数据。然而，SAR图像具有独特的结构与纹理特征，对习惯于电光学（EO）图像的分析人员来说难以解读。本文对比了包括Pix2Pix、CycleGAN、S-CycleGAN在内的先进生成对抗网络（GAN），并提出一种新型双生成器GAN，融合部分卷积与Transformer架构，逐步提升转换后图像的真实感，增强SAR数据的视觉可解释性。通过定性与定量评估，合成的EO图像在视觉保真度与特征保留方面均表现优异，显著提升了可读性。该技术在环境监测、城市规划及军事侦察等领域具有重要应用价值，推动了SAR与EO图像之间的信息鸿沟弥合，为遥感数据的广泛应用提供了新工具。
原文摘要 · Abstract (English)
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 dual?generator 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.
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