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Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks

Shahzad, Muhammad and Maurer, Michael and Fraundorfer, Friedrich and Wang, Yuanyuan and Zhu, Xiao Xiang (2019) Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 57 (2), pp. 1100-1116. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TGRS.2018.2864716 ISSN 0196-2892

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Abstract

This paper addresses the highly challenging prob-lem of automatically detecting man-made structures especiallybuildings in very high-resolution (VHR) synthetic apertureradar (SAR) images. In this context, this paper has two majorcontributions. First, it presents a novel and generic work-flow that initially classifies the spaceborne SAR tomography(TomoSAR) point clouds - generated by processing VHR SARimage stacks using advanced interferometric techniques knownas TomoSAR - into buildings and nonbuildings with the aid ofauxiliary information (i.e., either using openly available 2-Dbuilding footprints or adopting an optical image classificationscheme) and later back project the extracted building pointsonto the SAR imaging coordinates to produce automatic large-scale benchmark labeled (buildings/nonbuildings) SAR data sets.Second, these labeled data sets (i.e., building masks) have beenutilized to construct and train the state-of-the-art deep fullyconvolution neural networks with an additional conditionalrandom field represented as a recurrent neural network to detectbuilding regions in a single VHR SAR image. Such a cascadedformation has been successfully employed in computer vision andremote sensing fields for optical image classification but, to ourknowledge, has not been applied to SAR images. The resultsof the building detection are illustrated and validated over aTerraSAR-X VHR spotlight SAR image covering approximately39 km2 - almost the whole city of Berlin - with the mean pixelaccuracies of around 93.84%.

Item URL in elib:https://elib.dlr.de/122447/
Document Type:Article
Title:Buildings Detection 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, Friedrichfraundorfer (at) icg.tugraz.atUNSPECIFIED
Wang, Yuanyuanyuanyuan.wang (at) dlr.deUNSPECIFIED
Zhu, Xiao XiangDLR-IMF/TUM-LMFUNSPECIFIED
Date:2019
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:57
DOI :10.1109/TGRS.2018.2864716
Page Range:pp. 1100-1116
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:Synthetic aperture radar, Buildings, Feature extraction, Optical distortion, Optical sensors, Optical interferometry, Optical imaging, Building detection, fully convolution neural networks (CNNs), OpenStreetMap (OSM), synthetic aperture radar (SAR), SAR tomography (TomoSAR), TerraSAR-X/TanDEM-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 - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Hoffmann, Eike Jens
Deposited On:23 Oct 2018 14:28
Last Modified:21 Nov 2019 12:03

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