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

IMG2DSM: Height Simulation from Single Imagery Using Conditional Generative Adversarial Nets

Ghamisi, Pedram and Yokoya, Naoto (2018) IMG2DSM: Height Simulation from Single Imagery Using Conditional Generative Adversarial Nets. IEEE Geoscience and Remote Sensing Letters, 15 (5), pp. 794-798. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/LGRS.2018.2806945 ISBN 1545-598X ISSN 1545-598X

[img] PDF
2MB

Official URL: http://ieeexplore.ieee.org/document/8306501/

Abstract

This paper proposes a groundbreaking approach in the remote sensing community to simulating digital surface model (DSM) from a single optical image. This novel technique uses conditional generative adversarial nets whose architecture is based on an encoder-decoder network with skip connections (generator) and penalizing structures at the scale of image patches (discriminator). The network is trained on scenes where both DSM and optical data are available to establish an image-to-DSM translation rule. The trained network is then utilized to simulate elevation information on target scenes where no corresponding elevation information exists. The capability of the approach is evaluated both visually (in terms of photo interpretation) and quantitatively (in terms of reconstruction errors and classification accuracies) on sub-decimeter spatial resolution datasets captured over Vaihingen, Potsdam, and Stockholm. The results confirm the promising performance of the proposed framework.

Item URL in elib:https://elib.dlr.de/119293/
Document Type:Article
Title:IMG2DSM: Height Simulation from Single Imagery Using Conditional Generative Adversarial Nets
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Ghamisi, PedramDLR-IMF/TUM-LMFUNSPECIFIED
Yokoya, NaotoRIKENUNSPECIFIED
Date:2018
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:15
DOI :10.1109/LGRS.2018.2806945
Page Range:pp. 794-798
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
ISBN:1545-598X
Status:Published
Keywords:Conditional generative adversarial nets, convolutional neural network, deep learning, digital surface model (DSM), encoder-decoder nets, optical images
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: Ghamisi, Pedram
Deposited On:13 Mar 2018 12:11
Last Modified:31 Jul 2019 20:16

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.