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Automatic registration of a single SAR image and GIS building footprints in a large-scale urban area
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, su...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7694880/ https://www.ncbi.nlm.nih.gov/pubmed/33299267 http://dx.doi.org/10.1016/j.isprsjprs.2020.09.016 |
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author | Sun, Yao Montazeri, Sina Wang, Yuanyuan Zhu, Xiao Xiang |
author_facet | Sun, Yao Montazeri, Sina Wang, Yuanyuan Zhu, Xiao Xiang |
author_sort | Sun, Yao |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7694880 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-76948802020-12-07 Automatic registration of a single SAR image and GIS building footprints in a large-scale urban area Sun, Yao Montazeri, Sina Wang, Yuanyuan Zhu, Xiao Xiang ISPRS J Photogramm Remote Sens Article 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. Elsevier 2020-12 /pmc/articles/PMC7694880/ /pubmed/33299267 http://dx.doi.org/10.1016/j.isprsjprs.2020.09.016 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sun, Yao Montazeri, Sina Wang, Yuanyuan Zhu, Xiao Xiang Automatic registration of a single SAR image and GIS building footprints in a large-scale urban area |
title | Automatic registration of a single SAR image and GIS building footprints in a large-scale urban area |
title_full | Automatic registration of a single SAR image and GIS building footprints in a large-scale urban area |
title_fullStr | Automatic registration of a single SAR image and GIS building footprints in a large-scale urban area |
title_full_unstemmed | Automatic registration of a single SAR image and GIS building footprints in a large-scale urban area |
title_short | Automatic registration of a single SAR image and GIS building footprints in a large-scale urban area |
title_sort | automatic registration of a single sar image and gis building footprints in a large-scale urban area |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7694880/ https://www.ncbi.nlm.nih.gov/pubmed/33299267 http://dx.doi.org/10.1016/j.isprsjprs.2020.09.016 |
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