用地理嵌入提升卫星图像超分辨率，增强跨区域泛化能力
Can Location Embeddings Enhance Super-Resolution of Satellite Imagery?
- 引入地理位置嵌入，让模型理解不同地区的上下文信息
- 在建筑分割任务中显著优于现有方法，生成更连贯的高清图像
- 适合遥感分析、城市规划等需要跨区域通用性的场景
公开的卫星影像（如Sentinel-2）常因空间分辨率不足，难以支持城市规划与灾害响应等遥感任务的精确分析。当前超分辨率技术多基于有限数据集训练，导致在不同地理区域间泛化能力差。本文提出一种新型超分辨率框架，通过引入位置嵌入来捕捉地理上下文信息，提升模型跨区域适应性。该框架采用生成对抗网络（GANs），融合扩散模型技术以改善图像质量，并通过整合邻近影像信息缓解拼接伪影，实现无缝高分辨率输出。我们在建筑分割任务上验证了方法的有效性，结果表明其显著优于现有最先进方法，展现出在真实场景中的应用潜力。
原文摘要 · Abstract (English)
Publicly available satellite imagery, such as Sentinel- 2, often lacks the spatial resolution required for accurate analysis of remote sensing tasks including urban planning and disaster response. Current super-resolution techniques are typically trained on limited datasets, leading to poor generalization across diverse geographic regions. In this work, we propose a novel super-resolution framework that enhances generalization by incorporating geographic context through location embeddings. Our framework employs Generative Adversarial Networks (GANs) and incorporates techniques from diffusion models to enhance image quality. Furthermore, we address tiling artifacts by integrating information from neighboring images, enabling the generation of seamless, high-resolution outputs. We demonstrate the effectiveness of our method on the building segmentation task, showing significant improvements over state-of-the-art methods and highlighting its potential for real-world applications.
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