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Contrastive-Regulated CNN in the Complex Domain: A Method to Learn Physical Scattering Signatures From Flexible PolSAR Images

Zhao, Juanping and Datcu, Mihai and Zhang, Zenghui and Xiong, Huilin and Yu, Wenxian (2019) Contrastive-Regulated CNN in the Complex Domain: A Method to Learn Physical Scattering Signatures From Flexible PolSAR Images. IEEE Transactions on Geoscience and Remote Sensing, 57 (12), pp. 10116-10135. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TGRS.2019.2931620 ISSN 0196-2892

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Official URL: https://ieeexplore.ieee.org/document/8809406

Abstract

Single- and dual-polarimetric synthetic aperture radar (SAR) images provide very limited capabilities to interpret physical radar signatures. For generality and simplicity, we call single-polarimetric, dual-polarimetric, and fully polarimetric SAR (PolSAR) images flexible PolSAR images. In order to sufficiently extract physical scattering signatures from this kind of data and explore the potentials of different polarization modes on this task, this paper proposes a contrastive-regulated convolutional neural network (CNN) in the complex domain, attempting to learn a physically interpretable deep learning model directly from the original backscattered data. To achieve a better deep model containing physically interpretable parameters, the objective cost is compared to and selected from several commonly used loss functions in the complex form. The required ground-truth labels are generated automatically according to Cloude and Pottier's H-alpha division plane, which significantly reduces intensive labor cost and transfers this method to an unsupervised learning mechanism. The boundaries between different scattering signatures, however, sometimes show an erroneous separation. With the aim of aggregating intra-class instances and alienating inter-class instances, meanwhile, a complex-valued contrastive regularization term is computed mathematically and is added to the objective cost by a tradeoff factor. Moreover, data augmentation is applied to relieve the side effects caused by data imbalance. Finally, we performed experiments on German Aerospace Center's (DLR)'s L-band, high-resolution (HR), and airborne F-SAR data. Our results demonstrate the possibility of extracting physical scattering signatures from flexible PolSAR images. Physically interpretable potentials of SAR images with different polarization modes are analyzed, and we conclude with physical signature identification.

Item URL in elib:https://elib.dlr.de/131043/
Document Type:Article
Title:Contrastive-Regulated CNN in the Complex Domain: A Method to Learn Physical Scattering Signatures From Flexible PolSAR Images
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Zhao, JuanpingDLR-IMF; Department of Electric Information and Electronic Engineering, Shanghai Jiao Tong UniversityUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Zhang, ZenghuiDepartment of Electric Information and Electronic Engineering, Shanghai Jiao Tong UniversityUNSPECIFIED
Xiong, HuilinDepartment of Electric Information and Electronic Engineering, Shanghai Jiao Tong UniversityUNSPECIFIED
Yu, WenxianDepartment of Electric Information and Electronic Engineering, Shanghai Jiao Tong UniversityUNSPECIFIED
Date:December 2019
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:57
DOI :10.1109/TGRS.2019.2931620
Page Range:pp. 10116-10135
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:H-A-α target decomposition, complex-valued convolutional neural networks (CNNs), contrastive-regulated objective cost, data augmentation, physical scattering signatures, polarimetric synthetic aperture radar (PolSAR)
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 15:24
Last Modified:04 Dec 2019 15:27

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