用扩散模型修复遥感图像缺失数据，支持多波段一致性和地形引导。
A Diffusion-Based Framework for Terrain-Aware Remote Sensing Image Reconstruction
- 基于扩散模型，融合数字高程图作为条件输入，实现跨波段一致性重建
- 在云遮挡等大范围缺失下仍保持空间、光谱与时间一致性，优于传统方法
- 适合高分辨率遥感监测、灾后评估等需精准图像恢复的场景
遥感影像对环境监测、农业管理和灾害响应至关重要，但因云层遮盖、传感器故障或采集不完整导致的数据缺失——尤其在高分辨率和高频任务中——严重限制了其应用效果。传统插值方法难以处理大面积缺失和复杂结构。遥感影像包含多个具有不同含义的波段，保持波段间的一致性对避免合成图像异常至关重要。本文提出 SatelliteMaker，一种基于扩散模型的方法，可在不同缺失程度下重建数据，并维持空间、光谱与时间一致性。我们引入数字高程模型（DEM）作为条件输入，并设计定制化提示词，使扩散模型适用于定量遥感任务。此外，提出基于分布损失的 VGG-Adapter 模块，降低特征分布差异，确保风格一致性。大量实验表明，SatelliteMaker 在多项任务中达到当前最优性能。
原文摘要 · Abstract (English)
Remote sensing imagery is essential for environmental monitoring, agricultural management, and disaster response. However, data loss due to cloud cover, sensor failures, or incomplete acquisition-especially in high-resolution and high-frequency tasks-severely limits satellite imagery's effectiveness. Traditional interpolation methods struggle with large missing areas and complex structures. Remote sensing imagery consists of multiple bands, each with distinct meanings, and ensuring consistency across bands is critical to avoid anomalies in the combined images. This paper proposes SatelliteMaker, a diffusion-based method that reconstructs missing data across varying levels of data loss while maintaining spatial, spectral, and temporal consistency. We also propose Digital Elevation Model (DEM) as a conditioning input and use tailored prompts to generate realistic images, making diffusion models applicable to quantitative remote sensing tasks. Additionally, we propose a VGG-Adapter module based on Distribution Loss, which reduces distribution discrepancy and ensures style consistency. Extensive experiments show that SatelliteMaker achieves state-of-the-art performance across multiple tasks.
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