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DSM-to-LoD2: Spaceborne Stereo Digital Surface Model Refinement

Bittner, Ksenia and d'Angelo, Pablo and Körner, Marco and Reinartz, Peter (2018) DSM-to-LoD2: Spaceborne Stereo Digital Surface Model Refinement. Remote Sensing, 10 (1926), pp. 1-20. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs10121926 ISSN 2072-4292

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Official URL: https://www.mdpi.com/2072-4292/10/12/1926

Abstract

A digital surface model (DSM) provides the geometry and structure of an urban environment with buildings being the most prominent objects in it. Built-up areas change with time due to the rapid expansion of cities. New buildings are being built, existing ones are expanded, and old buildings are torn down. As a result, 3D surface models can increase the understanding and explanation of complex urban scenarios. They are very useful in numerous fields of remote sensing applications, in tasks related to 3D reconstruction and city modeling, planning, visualization, disaster management, navigation, and decision-making, among others. DSMs are typically derived from various acquisition techniques, like photogrammetry, laser scanning, or synthetic aperture radar (SAR). The generation of DSMs from very high resolution optical stereo satellite imagery leads to high resolution DSMs which often suffer from mismatches, missing values, or blunders, resulting in coarse building shape representation. To overcome these problems, we propose a method for 3D surface model generation with refined building shapes to level of detail (LoD) 2 from stereo half-meter resolution satellite DSMs using deep learning techniques. Mainly, we train a conditional generative adversarial network (cGAN) with an objective function based on least square residuals to generate an accurate LoD2-like DSM with enhanced 3D object shapes directly from the noisy stereo DSM input. In addition, to achieve close to LoD2 shapes of buildings, we introduce a new approach to generate an artificial DSM with accurate and realistic building geometries from city geography markup language (CityGML) data, on which we later perform a training of the proposed cGAN architecture. The experimental results demonstrate the strong potential to create large-scale remote sensing elevation models where the buildings exhibit better-quality shapes and roof forms than just using the matching process. Moreover, the developed model is successfully applied to a different city that is unseen during the training to show its generalization capacity.

Item URL in elib:https://elib.dlr.de/124505/
Document Type:Article
Title:DSM-to-LoD2: Spaceborne Stereo Digital Surface Model Refinement
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Bittner, KseniaKsenia.Bittner (at) dlr.dehttps://orcid.org/0000-0002-4048-3583
d'Angelo, Pablopablo.angelo (at) dlr.dehttps://orcid.org/0000-0001-8541-3856
Körner, Marcomarco.koerner (at) tum.deUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475
Date:30 November 2018
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:10
DOI :10.3390/rs10121926
Page Range:pp. 1-20
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:conditional generative adversarial networks; digital surface model; 3D scene refinement; 3D building shape; urban region
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), R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Zielske, Mandy
Deposited On:07 Dec 2018 17:07
Last Modified:20 Nov 2019 07:22

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