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

PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field

Bi, Haixia and Yao, Jing and Wei, Zhiqiang and Hong, Danfeng and Chanussot, Jocelyn (2022) PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field. IEEE Geoscience and Remote Sensing Letters, 19, p. 4005205. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2020.3034700. ISSN 1545-598X.

[img] PDF - Preprint version (submitted draft)
2MB

Official URL: https://ieeexplore.ieee.org/abstract/document/9252858

Abstract

Polarimetric synthetic aperture radar (PolSAR) image classification has been investigated vigorously in various remote sensing applications. However, it is still a challenging task nowadays. One significant barrier lies in the speckle effect embedded in the PolSAR imaging process, which greatly degrades the quality of the images and further complicates the classification. To this end, we present a novel PolSAR image classification method that removes speckle noise via low-rank (LR) feature extraction and enforces smoothness priors via the Markov random field (MRF). Especially, we employ the mixture of Gaussian-based robust LR matrix factorization to simultaneously extract discriminative features and remove complex noises.Then, a classification map is obtained by applying a convolutional neural network with data augmentation on the extracted features, where local consistency is implicitly involved, and the insufficient label issue is alleviated. Finally, we refine the classificationmap by MRF to enforce contextual smoothness. We conduct experiments on two benchmark PolSAR data sets. Experimental results indicate that the proposed method achieves promising classification performance and preferable spatial consistency.

Item URL in elib:https://elib.dlr.de/138283/
Document Type:Article
Title:PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Bi, HaixiaFaculty of Engineering, University of Bristol, Bristol BS8 1UB, United KingdomUNSPECIFIED
Yao, JingJing.Yao (at) dlr.deUNSPECIFIED
Wei, Zhiqiange Xi’an Electronics and Engineering InstituteUNSPECIFIED
Hong, DanfengDanfeng.Hong (at) dlr.deUNSPECIFIED
Chanussot, Jocelyninstitute nationale polytechnique de grenobleUNSPECIFIED
Date:2022
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:19
DOI :10.1109/LGRS.2020.3034700
Page Range:p. 4005205
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
Status:Published
Keywords:Convolutional neural network (CNN), low-rank(LR) matrix factorization, Markov random field (MRF), mixtureof Gaussian (MoG), polarimetric synthetic aperture radar (Pol-SAR) image classification
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
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Liu, Rong
Deposited On:26 Nov 2020 09:47
Last Modified:20 Dec 2021 17:35

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.