DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images

Sun, Yao and Hua, Yuansheng and Mou, LiChao and Zhu, Xiao Xiang (2022) CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images. IEEE Transactions on Geoscience and Remote Sensing, 60, p. 5201215. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2020.3043089. ISSN 0196-2892.

[img] PDF - Published version

Official URL: https://ieeexplore.ieee.org/document/9321533


Object retrieval and reconstruction from very high resolution (VHR) synthetic aperture radar (SAR) images are of great importance for urban SAR applications, yet highly challenging owing to the complexity of SAR data. This paper addresses the issue of individual building segmentation from a single VHR SAR image in large-scale urban areas. To achieve this, we introduce building footprints from GIS data as complementary information and propose a novel conditional GIS-aware network (CG-Net). The proposed model learns multi-level visual features and employs building footprints to normalize the features for predicting building masks in the SAR image. We validate our method using a high resolution spotlight TerraSAR-X image collected over Berlin. Experimental results show that the proposed CG-Net effectively brings improvements with variant backbones. We further compare two representations of building footprints, namely complete building footprints and sensor-visible footprint segments, for our task, and conclude that the use of the former leads to better segmentation results. Moreover, we investigate the impact of inaccurate GIS data on our CG-Net, and this study shows that CG-Net is robust against positioning errors in GIS data. In addition, we propose an approach of ground truth generation of buildings from an accurate digital elevation model (DEM), which can be used to generate large-scale SAR image datasets. The segmentation results can be applied to reconstruct 3D building models at level-of-detail (LoD) 1, which is demonstrated in our experiments.

Item URL in elib:https://elib.dlr.de/138029/
Document Type:Article
Title:CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Sun, YaoYao.Sun (at) dlr.deUNSPECIFIED
Hua, YuanshengYuansheng.Hua (at) dlr.deUNSPECIFIED
Mou, LiChaoLiChao.Mou (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:January 2022
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/TGRS.2020.3043089
Page Range:p. 5201215
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:deep convolutional neural network (CNN), GIS, individual building segmentation, large-scale urban areas, synthetic aperture radar (SAR)
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Sun, Yao
Deposited On:27 Nov 2020 17:47
Last Modified:09 Dec 2021 18:40

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.