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Artificial generation of big data for improving image classification: a generative adversarial network approach on SAR data

Marmanis, Dimitrios and Yao, Wei and Adam, Fathalrahman and Datcu, Mihai and Reinartz, Peter and Schindler, Konrad and Wegner, Jan D. and Stilla, Uwe (2017) Artificial generation of big data for improving image classification: a generative adversarial network approach on SAR data. Big data from space 2017, 28. - 30. Nov. 2017, Toulouse, France.

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Abstract

Very High Spatial Resolution (VHSR) large-scale SAR image databases are still an unresolved issue in the Remote Sensing field. In this work, we propose such a dataset and use it to explore patch-based classification in urban and periurban areas, considering 7 distinct semantic classes. In this context, we investigate the accuracy of large CNN classification models and pre-trained networks for SAR imaging systems. Furthermore, we propose a Generative Adversarial Network (GAN) for SAR image generation and test, whether the synthetic data can actually improve classification accuracy.

Item URL in elib:https://elib.dlr.de/117831/
Document Type:Conference or Workshop Item (Speech)
Title:Artificial generation of big data for improving image classification: a generative adversarial network approach on SAR data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Marmanis, DimitriosDimitrios.Marmanis (at) dlr.deUNSPECIFIED
Yao, WeiWei.Yao (at) dlr.deUNSPECIFIED
Adam, FathalrahmanFathalrahman.Adam (at) dlr.deUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.deUNSPECIFIED
Schindler, Konradkonrad.schindler (at) geod.baug.ethz.chUNSPECIFIED
Wegner, Jan D.jan.wegner (at) geod.baug.ethz.chUNSPECIFIED
Stilla, Uwestilla (at) tum.deUNSPECIFIED
Date:2017
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-4
Status:Published
Keywords:Big Data, SAR classification, GANs, Generative Adversarial Networks, Deep Learning
Event Title:Big data from space 2017
Event Location:Toulouse, France
Event Type:international Conference
Event Dates:28. - 30. Nov. 2017
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 > Photogrammetry and Image Analysis
German Remote Sensing Data Center > Land Surface
Deposited By: Yao, Wei
Deposited On:08 Jan 2018 13:08
Last Modified:31 Jul 2019 20:15

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