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Automatic feature-based geometric fusion of multi-view TomoSAR point clouds in urban area

Wang, Yuanyuan and Zhu, Xiao Xiang (2015) Automatic feature-based geometric fusion of multi-view TomoSAR point clouds in urban area. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (3), pp. 953-965. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/JSTARS.2014.2361430 ISSN 1939-1404

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Official URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6942160

Abstract

Interferometric synthetic aperture radar (InSAR) techniques, such as persistent scatterer interferometry (PSI) or SAR tomography (TomoSAR), deliver three-dimensional (3-D) point clouds of the scatterers’ positions together with their motion information relative to a reference point. Due to the SAR sidelooking geometry, minimum of two point clouds from crossheading orbits, i.e., ascending and descending, are required to achieve a complete monitoring over an urban area. However, these two point clouds are usually not coregistered due to their different reference points with unknown 3-D positions. In general, no exact identical points from the same physical object can be found in such two point clouds. This article describes a robust algorithm for fusing such two point clouds of urban areas. The contribution of this paper is finding the theoretically exact point correspondence, which is the end positions of façades, where the two point clouds close. We explicitly define this algorithm as “L-shape detection and matching,” in this paper, because the façades commonly appear as L-shapes in InSAR point cloud. This algorithm introduces a few important features for a reliable result, including point density estimation using adaptive directional window for better façade points detection and L-shape extraction using weighed Hough transform. The algorithm is fully automatic. Its accuracy is evaluated using simulated data. Furthermore, the proposed method is applied on two TomoSAR point clouds over Berlin with ascending and descending geometry. The result is compared with the first PSI point cloud fusion method (S. Gernhardt and R. Bamler, “Deformation monitoring of single buildings using meter-resolution SAR data in PSI,” ISPRS J. Photogramm. Remote Sens., vol. 73, pp. 68–79, 2012.) for urban area. Submeter consistency is achieved.

Item URL in elib:https://elib.dlr.de/93032/
Document Type:Article
Title:Automatic feature-based geometric fusion of multi-view TomoSAR point clouds in urban area
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Wang, YuanyuanTUMUNSPECIFIED
Zhu, Xiao XiangDLR-IMF/TUM-LMFUNSPECIFIED
Date:2015
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:8
DOI :10.1109/JSTARS.2014.2361430
Page Range:pp. 953-965
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:Published
Keywords:point cloud fusion, SAR tomography, SAR, TerraSAR-X
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Wang, Yuanyuan
Deposited On:03 Dec 2014 18:17
Last Modified:31 Jul 2019 19:50

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