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

Extraction of Buildings in VHR SAR Images using fully Convolution Neural Networks

Shahzad, Muhammad and Maurer, Michael and Fraundorfer, Friedrich and Wang, Yuanyuan and Zhu, Xiao Xiang (2018) Extraction of Buildings in VHR SAR Images using fully Convolution Neural Networks. In: 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4367-4370. IGARSS 2018, 22.-27. Juli 2018, Valencia, Spanien.

[img] PDF
642kB

Official URL: https://www.igarss2018.org/

Abstract

Modern spaceborne synthetic aperture radar (SAR) sensors, such as TerraSAR-X/TanDEM-X and COSMO-SkyMed, can deliver very high resolution (VHR) data beyond the inherent spatial scales (on the order of 1m) of buildings, constituting invaluable data source for large-scale urban mapping. Processing this VHR data with advanced interferometric techniques, such as SAR tomography (TomoSAR), enables the generation of 3-D (or even 4-D) TomoSAR point clouds from space. In this paper, we present a novel and generic workflow that exploits these TomoSAR point clouds in a way that is capable to automatically produce benchmark annotated (buildings/nonbuildings) SAR datasets. These annotated datasets (building masks) have been utilized to construct and train the state-ofthe-art deep Fully Convolution Neural Networks with an additional Conditional Random Field represented as a Recurrent Neural Network to detect building regions in a single VHR SAR image. The results of building detection are illustrated and validated over TerraSAR-X VHR spotlight SAR image covering approximately 39 km2- almost the whole city of Berlin - with mean pixel accuracies of around 93.84%.

Item URL in elib:https://elib.dlr.de/123939/
Document Type:Conference or Workshop Item (Speech)
Title:Extraction of Buildings in VHR SAR Images using fully Convolution Neural Networks
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Shahzad, MuhammadMuhammad.Shahzad (at) dlr.deUNSPECIFIED
Maurer, MichaelTU GrazUNSPECIFIED
Fraundorfer, Friedrichfriedrich.fraundorfer (at) dlr.deUNSPECIFIED
Wang, Yuanyuantum, Yuanyuan.Wang (at) dlr.dehttps://orcid.org/0000-0002-0586-9413
Zhu, Xiao Xiangxiaoxiang.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Date:July 2018
Journal or Publication Title:2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 4367-4370
Status:Published
Keywords:very high resolution (VHR) data, SAR, Neural networks
Event Title:IGARSS 2018
Event Location:Valencia, Spanien
Event Type:international Conference
Event Dates:22.-27. Juli 2018
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - Vabene++ (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Remote Sensing Technology Institute > EO Data Science
Deposited By: Zielske, Mandy
Deposited On:30 Nov 2018 14:34
Last Modified:31 Jul 2019 20:21

Repository Staff Only: item control page

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