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

Learning Physical Scattering Patterns from POLSAR Images By Using Complex-Valued CNN

Zhao, Juanping and Datcu, Mihai and Zhang, Zenghui and Xiong, Huilin and Yu, Wenxian (2019) Learning Physical Scattering Patterns from POLSAR Images By Using Complex-Valued CNN. IGARSS 2019, 28 Jul-02 Aug 2019, Yokohama, Japan.

[img] PDF

Official URL: https://igarss2019.org/Papers/ViewPapers_MS.asp?PaperNum=3824


Full-polarimetric synthetic aperture radar (SAR) images have the ability to provide physical patterns of the earth observation, no more than geometric information. In order to learn physical patterns from non-full-polarimetric SAR images, a complex-valued CNN is leveraged to learn a model containing physical parameters. The parameters are learned from the original complex scattering matrix of full-polarimetric SAR images and they can be adopted to extract physical patterns from non-full-polarimetric SAR images. Cloude and Pottier’s H-α division, as the annotation principle, is computed by way of coherence matrix. We perform experiments on (German Aerospace Center) DLR’s full-polarimetric, airborne F-SAR data, demonstrating that extracting physical patterns from non-full-polarimetric images is feasible. The comparative results illustrate that: 1) The best physical categoric patterns can be extracted from HV and VH polarimetric images in general, while performance from HH and VV polarimetric images are limited; 2) Cross-polarimetric SAR images have greater ability for surface and volume scattering, while co-polarimetric ones are better for multiple scattering extraction.

Item URL in elib:https://elib.dlr.de/130535/
Document Type:Conference or Workshop Item (Poster)
Title:Learning Physical Scattering Patterns from POLSAR Images By Using Complex-Valued CNN
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Zhang, Zenghuizenghui.zhang (at) sjtu.edu.cnUNSPECIFIED
Xiong, HuilinShanghai Jiao Tong UniversityUNSPECIFIED
Yu, WenxianShanghai Jiao Tong UniversityUNSPECIFIED
Date:31 January 2019
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1-4
Keywords:Polarimetric synthetic aperture radar (PolSAR) images, physical scattering pattern, complex-valued convolutional neural network (CNN), H-A-α target decomposition, F-SAR
Event Title:IGARSS 2019
Event Location:Yokohama, Japan
Event Type:international Conference
Event Dates:28 Jul-02 Aug 2019
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 > EO Data Science
Deposited By: Karmakar, Chandrabali
Deposited On:04 Dec 2019 14:16
Last Modified:04 Dec 2019 14:16

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.