Automatic registration of SAR image and GIS building footprints data in dense urban area

Yao Sun; Yuanyuan Wang; Xiao Xiang Zhu

doi:10.1109/IGARSS.2019.8900187

Automatic registration of SAR image and GIS building footprints data in dense urban area

Chair of Data Science in Earth Observation

Deutsches Zentrum für Luft- und Raumfahrt (DLR)
Technical University of Munich

Research output: Contribution to conference › Paper › peer-review

3 Scopus citations

Abstract

In this paper, we propose a framework for the automatic registration of GIS building footprint polygons to a corresponding SAR image through the corresponding features of building walls in the two data. To extract feature lines, the Potts model is adopted for SAR image segmentation, and visibility test is performed on both data. The feature lines are then sampled to two point sets, and are registered using Iterative Closest Point (ICP) algorithm. The test result shows a registration accuracy of 0.67 m in azimuth direction, and 1.64m in range direction.

Original language	English
Pages	927-930
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS.2019.8900187
State	Published - 2019
Event	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan Duration: 28 Jul 2019 → 2 Aug 2019

Conference

Conference	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/Territory	Japan
City	Yokohama
Period	28/07/19 → 2/08/19

Keywords

GIS building footprints
Registration
SAR
Urban area

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10.1109/IGARSS.2019.8900187

Cite this

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title = "Automatic registration of SAR image and GIS building footprints data in dense urban area",

abstract = "In this paper, we propose a framework for the automatic registration of GIS building footprint polygons to a corresponding SAR image through the corresponding features of building walls in the two data. To extract feature lines, the Potts model is adopted for SAR image segmentation, and visibility test is performed on both data. The feature lines are then sampled to two point sets, and are registered using Iterative Closest Point (ICP) algorithm. The test result shows a registration accuracy of 0.67 m in azimuth direction, and 1.64m in range direction.",

keywords = "GIS building footprints, Registration, SAR, Urban area",

author = "Yao Sun and Yuanyuan Wang and Zhu, \{Xiao Xiang\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",

year = "2019",

doi = "10.1109/IGARSS.2019.8900187",

language = "English",

pages = "927--930",

}

TY - CONF

T1 - Automatic registration of SAR image and GIS building footprints data in dense urban area

AU - Sun, Yao

AU - Wang, Yuanyuan

AU - Zhu, Xiao Xiang

PY - 2019

Y1 - 2019

N2 - In this paper, we propose a framework for the automatic registration of GIS building footprint polygons to a corresponding SAR image through the corresponding features of building walls in the two data. To extract feature lines, the Potts model is adopted for SAR image segmentation, and visibility test is performed on both data. The feature lines are then sampled to two point sets, and are registered using Iterative Closest Point (ICP) algorithm. The test result shows a registration accuracy of 0.67 m in azimuth direction, and 1.64m in range direction.

AB - In this paper, we propose a framework for the automatic registration of GIS building footprint polygons to a corresponding SAR image through the corresponding features of building walls in the two data. To extract feature lines, the Potts model is adopted for SAR image segmentation, and visibility test is performed on both data. The feature lines are then sampled to two point sets, and are registered using Iterative Closest Point (ICP) algorithm. The test result shows a registration accuracy of 0.67 m in azimuth direction, and 1.64m in range direction.

KW - GIS building footprints

KW - Registration

KW - SAR

KW - Urban area

UR - https://www.scopus.com/pages/publications/85092349868

U2 - 10.1109/IGARSS.2019.8900187

DO - 10.1109/IGARSS.2019.8900187

M3 - Paper

AN - SCOPUS:85092349868

SP - 927

EP - 930

T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019

Y2 - 28 July 2019 through 2 August 2019

ER -

Automatic registration of SAR image and GIS building footprints data in dense urban area

Abstract

Conference

Keywords

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