Automated 3D recovery from very high resolution multi-view satellite images
Automated 3D recovery from very high resolution multi-view satellite images
复制标题
从极高分辨率多视图卫星图像中自动进行 3D 恢复
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
R. Qin
中科院分区：
文献类型：
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作者：
R. Qin
本文提出了一种自动流水线处理多视角卫星图像的三维数字表面模型（DSM）。拟议的管道执行自动地理参考，并生成高质量的密集匹配点云。特别是，开发了一种新的方法，该方法融合了通过立体匹配得到的多个深度图，以生成高质量的3D图。通过从样本LiDAR数据中学习立体对的关键配置，我们根据结果与样本数据的接近程度对图像对进行排名。多个深度图从各个图像对的融合与自适应3D中值滤波器，认为图像的光谱相似性。我们证明，所提出的自适应中值滤波器通常提供更好的结果，一般相比，正常的中值滤波器，并实现了0.36米的RMSE在最好的情况下，改善的精度。详细介绍了试验结果和分析。
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.