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在大规模城市区域中对单幅合成孔径雷达(SAR)图像与地理信息系统(GIS)建筑物足迹进行自动配准。

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

作者信息

Sun Yao, Montazeri Sina, Wang Yuanyuan, Zhu Xiao Xiang

机构信息

Remote Sensing Technology Institute, German Aerospace Center (DLR), Münchener Straße 20, 82234 Weßling, Germany.

Signal Processing in Earth Observation, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany.

出版信息

ISPRS J Photogramm Remote Sens. 2020 Dec;170:1-14. doi: 10.1016/j.isprsjprs.2020.09.016.

DOI:10.1016/j.isprsjprs.2020.09.016
PMID:33299267
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7694880/
Abstract

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.

摘要

现有的从合成孔径雷达(SAR)图像进行建筑物三维重建的技术大多基于多基线SAR干涉测量法,如永久散射体干涉测量法(PSI)和SAR层析成像(TomoSAR)。然而,这些技术需要数十幅图像才能进行可靠的重建,这限制了它们在各种场景中的应用,如应急响应。因此,使用单幅SAR图像和来自地理信息系统(GIS)数据的建筑物轮廓的替代方法在三维重建中显示出巨大潜力。GIS数据和SAR图像的结合需要精确配准,由于地形高度未知以及难以找到和提取对应关系,这具有挑战性。在本文中,我们提出了一个框架,通过利用表示地面与可见建筑物立面相交处的特征,特别是建筑物多边形中的近距边界以及SAR图像中的双程反射线,将GIS建筑物轮廓自动配准到SAR图像上。基于这些特征,两个数据集在多个分辨率下逐步配准,使算法能够应对局部地形的变化。所提出的框架在柏林使用一幅TerraSAR-X高分辨率聚束模式图像和该地区的GIS建筑物轮廓进行了测试。与地面真值相比,所提出的算法将平均距离误差从配准前的5.91米降低到了-0.08米,标准差从2.77米降低到了1.12米。这样的精度优于典型城市楼层高度(3米)的一半,对于大规模精确建筑物高度重建具有重要意义。所提出的配准框架在协助典型城市地区的SAR图像解释以及从SAR图像重建建筑物模型方面具有巨大潜力。

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本文引用的文献

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2
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.随机松弛,吉布斯分布,以及贝叶斯图像恢复。
IEEE Trans Pattern Anal Mach Intell. 1984 Jun;6(6):721-41. doi: 10.1109/tpami.1984.4767596.
3
Evaluation of stereo matching costs on images with radiometric differences.具有辐射差异的图像上立体匹配成本的评估。
IEEE Trans Pattern Anal Mach Intell. 2009 Sep;31(9):1582-99. doi: 10.1109/TPAMI.2008.221.
4
Stereo processing by semiglobal matching and mutual information.通过半全局匹配和互信息进行立体处理。
IEEE Trans Pattern Anal Mach Intell. 2008 Feb;30(2):328-41. doi: 10.1109/TPAMI.2007.1166.