This paper presents an automated pipeline for processing multi-view satellite
images to 3D digital surface models (DSM). The proposed pipeline performs
automated geo-referencing and generates high-quality densely matched point
clouds. In particular, a novel approach is developed that fuses multiple depth
maps derived by stereo matching to generate high-quality 3D maps. By learning
critical configurations of stereo pairs from sample LiDAR data, we rank the
image pairs based on the proximity of the results to the sample data. Multiple
depth maps derived from individual image pairs are fused with an adaptive 3D
median filter that considers the image spectral similarities. We demonstrate
that the proposed adaptive median filter generally delivers better results in
general as compared to normal median filter, and achieved an accuracy of
improvement of 0.36 meters RMSE in the best case. Results and analysis are
introduced in detail.

本文提出了一种处理多视图卫星图像生成 3D 数字表面模型的自动化流程，其中包括自动地地理参考和基于匹配生成高质量密集点云。通过学习样本 LiDAR 数据的关键配置，我们根据结果的接近程度对图像对进行排名，并使用自适应 3D 中值滤波器融合多个深度图。我们证明了提出的自适应中值滤波器通常比普通中值滤波器产生更好的结果，在最佳情况下达到了 0.36 米 RMSE 的精度提高。结果和分析被详细介绍。