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Urban Area Analysis in Single-polarized SAR Images Based On Unsupervised Deep Learning

Huang, Zhongling and Datcu, Mihai (2021) Urban Area Analysis in Single-polarized SAR Images Based On Unsupervised Deep Learning. In: 13th European Conference on Synthetic Aperture Radar, EUSAR 2021, pp. 1-5. VDE Verlag. EUSAR 2021, 29-01 Apr. 2021, Leipzig, Germany. ISBN 978-380075457-1. ISSN 2197-4403.

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Official URL: https://ieeexplore.ieee.org/document/9472590

Abstract

Urban mapping from remote sensing images is important for monitoring urbanization. In this paper, we propose an unsupervised learning algorithm for high-resolution single-polarized synthetic aperture radar (SAR) image to extract man-made targets for urban area analysis. The proposed method mainly focuses on the special physical characteristics of man-made targets that are different from natural areas. Without polarimetric information, we propose the sub-band scattering pattern based on time-frequency analysis to describe the physical properties of targets, and then design an end-to-end neural network to learn the latent features and potential clusters. The proposed method is evaluated on three different urban areas acquired at C-band by Sentinel-1 and Gaofen-3, and X-band by TerraSAR-X, respectively. The experiments present the visualized result of man-made targets extraction and analyze some specific targets to show the effectiveness of our proposed method.

Item URL in elib:https://elib.dlr.de/144971/
Document Type:Conference or Workshop Item (Speech)
Title:Urban Area Analysis in Single-polarized SAR Images Based On Unsupervised Deep Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Huang, ZhonglingChinese Academy of Sciences, Beijing, ChinaUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Date:1 March 2021
Journal or Publication Title:13th European Conference on Synthetic Aperture Radar, EUSAR 2021
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Page Range:pp. 1-5
Editors:
EditorsEmailEditor's ORCID iD
Datcu, MihaiMihai.Datcu@dlr.deUNSPECIFIED
Publisher:VDE Verlag
ISSN:2197-4403
ISBN:978-380075457-1
Status:Published
Keywords:urban area, unsupervised deep learning
Event Title:EUSAR 2021
Event Location:Leipzig, Germany
Event Type:international Conference
Event Dates:29-01 Apr. 2021
Organizer:VDE
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - SAR methods, R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Otgonbaatar, Soronzonbold
Deposited On:12 Nov 2021 11:57
Last Modified:01 May 2022 03:00

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