Digital Surface Models (DSM) offer a wealth of height information for understanding the Earth's surface as well as monitoring the existence or change in natural and man-made structures. Classical height estimation requires multi-view geospatial imagery or LiDAR point clouds which can be expensive to acquire. Single-view height estimation using neural network based models shows promise however it can struggle with reconstructing high resolution features. The latest advancements in diffusion models for high resolution image synthesis and editing have yet to be utilized for remote sensing imagery, particularly height estimation. Our approach involves training a generative diffusion model to learn the joint distribution of optical and DSM images across both domains as a Markov chain. This is accomplished by minimizing a denoising score matching objective while being conditioned on the source image to generate realistic high resolution 3D surfaces. In this paper we experiment with conditional denoising diffusion probabilistic models (DDPM) for height estimation from a single remotely sensed image and show promising results on the Vaihingen benchmark dataset.

翻译：数字表面模型（DSM）提供了丰富的地球表面高度信息，可用于监测自然及人造结构的存在或变化。传统高度估计方法需要多视角地理空间影像或激光雷达点云数据，其获取成本高昂。基于神经网络的单视图高度估计方法虽展现出潜力，但在重建高分辨率特征方面存在困难。扩散模型在高分辨率图像合成与编辑领域的最新进展尚未被应用于遥感影像处理，尤其是高度估计任务。我们的方法通过训练生成式扩散模型，以马尔可夫链形式学习光学影像与DSM图像在双域中的联合分布。通过最小化去噪评分匹配目标，并基于源图像进行条件约束，生成逼真的高分辨率三维表面。本文采用条件去噪扩散概率模型（DDPM）对单幅遥感影像进行高度估计，在Vaihingen基准数据集上取得了具有前景的实验结果。