Automatic registration of a single SAR image and GIS building footprints in a large-scale urban area

基本事实 地形 计算机科学 交叉口(航空) 比例(比率) 遥感 计算机视觉 合成孔径雷达 人工智能 航程(航空) 图像(数学) 地理 地图学 复合材料 材料科学
作者
Yeneng Sun,Sina Montazeri,Yuanyuan Wang,Xiao Xiang Zhu
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing 卷期号:170: 1-14 被引量:17
标识
DOI:10.1016/j.isprsjprs.2020.09.016
摘要

Existing techniques of 3-D reconstruction of buildings from SAR images are mostly based on multibaseline SAR interferometry, such as PSI and SAR tomography (TomoSAR). However, these techniques require tens of images for a reliable reconstruction, which limits the application in various scenarios, such as emergency response. Therefore, alternatives that use a single SAR image and the building footprints from GIS data show their great potential in 3-D reconstruction. The combination of GIS data and SAR images requires a precise registration, which is challenging due to the unknown terrain height, and the difficulty in finding and extracting the correspondence. In this paper, we propose a framework to automatically register GIS building footprints to a SAR image by exploiting the features representing the intersection of ground and visible building facades, specifically the near-range boundaries in the building polygons, and the double bounce lines in the SAR image. Based on those features, the two data sets are registered progressively in multiple resolutions, allowing the algorithm to cope with variations in the local terrain. The proposed framework was tested in Berlin using one TerraSAR-X High Resolution SpotLight image and GIS building footprints of the area. Comparing to the ground truth, the proposed algorithm reduced the average distance error from 5.91 m before the registration to −0.08 m, and the standard deviation from 2.77 m to 1.12 m. Such accuracy, better than half of the typical urban floor height (3 m), is significant for precise building height reconstruction on a large scale. The proposed registration framework has great potential in assisting SAR image interpretation in typical urban areas and building model reconstruction from SAR images.

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