用轻量方法控制高分辨率卫星图生成，提升几何精度。
Efficient Geometry-Controlled High-Resolution Satellite Image Synthesis
- 仅用跳连特征+窗口交叉注意力实现几何控制
- 生成图像与控制地图对齐度优于现有方法
- 适合遥感、灾害监测等需精准地理信息的场景
高分辨率卫星图像在偏远地区或罕见事件中往往稀缺且昂贵，制约了土地覆盖分类、变化检测和灾害监测等机器学习模型的开发与测试。本文通过引入几何控制机制，改进已有预训练扩散模型的图像合成过程。提出一种简单高效的方法，仅利用跳连特征与窗口化交叉注意力模块即可实现对合成过程的精确控制。对比多种已有控制技术表明，该方法在性能相当的前提下，显著提升了生成图像与几何控制地图的一致性。同时指出当前评估方法的局限性，强调建立统一对齐评估标准的重要性。
原文摘要 · Abstract (English)
High-resolution satellite images are often scarce and costly, especially for remote areas or infrequent events. This shortage hampers the development and testing of machine learning models for land-cover classification, change detection, and disaster monitoring. In this paper, we tackle the problem of geometry-controlled high-resolution satellite image synthesis by adding control over existing pre-trained diffusion models. We propose a simple yet efficient method for controlling the synthesis process by leveraging only skip connection features using windowed cross-attention modules. Several previously established control techniques are compared, indicating that our method achieves comparable performance while leading to a better alignment with the geometry control map. We also discuss the limitations in current evaluation approaches, amplifying the necessity of a consistent alignment assessment.
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