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An Unsupervised Remote Sensing Change Detection Method Based on Multiscale Graph Convolutional Network and Metric Learning

Tang, Xu and Zhang, Huayu and Mou, LiChao and Liu, Fang and Zhang, Xiangrong and Zhu, Xiao Xiang and Jiao, Licheng (2022) An Unsupervised Remote Sensing Change Detection Method Based on Multiscale Graph Convolutional Network and Metric Learning. IEEE Transactions on Geoscience and Remote Sensing, pp. 1-15. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2021.3106381. ISSN 0196-2892. (In Press)

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Official URL: https://ieeexplore.ieee.org/document/9526855

Abstract

As a fundamental application, change detection (CD) is widespread in the remote sensing (RS) community. With the increase in the spatial resolution of RS images, high-resolution remote sensing (HRRS) image CD tasks receive growing attention. The change information hidden in multitemporal HRRS images could help discover our planet comprehensively. In the current deep learning era, convolutional neural networks (CNNs) have become one of the most powerful tools for a wide range of RS tasks including HRRS image CD, due to their superb feature learning capacity. However, most of them need a large amount of labeled data to accomplish the CD process, which is challenging or even impractical in many RS applications. Also, given the limited valid receptive field, CNNs can only capture short-range context within HRRS images, which is probably not enough to fully explore change information from the images. To overcome these limitations, in this article, we propose an unsupervised CD method, termed GMCD, based on graph convolutional network (GCN) and metric learning. GMCD consists of a Siamese fully convolution network (FCN), a multiscale dynamic GCN (Mlt-GCN), and a pseudolabel generation mechanism based on metric learning. The Siamese FCN contains a Siamese encoder and a pyramid-shaped decoder, aiming to extract multiscale features and integrate them to generate reliable difference images (DIs). Mlt-GCN focuses on capturing the short- and long-range contextual patterns at feature map level to extract changed and unchanged areas completely. The pseudolabel generation mechanism aims to produce reliable pseudolabels (changed, unchanged, and uncertain) to help accomplish the model training in an unsupervised way. Experiments on four HRRS image CD datasets demonstrate that GMCD outperforms the existing state-of-the-art methods.

Item URL in elib:https://elib.dlr.de/145757/
Document Type:Article
Title:An Unsupervised Remote Sensing Change Detection Method Based on Multiscale Graph Convolutional Network and Metric Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Tang, XuKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian UniversityUNSPECIFIED
Zhang, HuayuKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian UniversityUNSPECIFIED
Mou, LiChaoLiChao.Mou (at) dlr.dehttps://orcid.org/0000-0001-8407-6413
Liu, FangKey Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and TechnologyUNSPECIFIED
Zhang, XiangrongKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian UniversityUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Jiao, LichengKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian UniversityUNSPECIFIED
Date:2022
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1109/TGRS.2021.3106381
Page Range:pp. 1-15
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:In Press
Keywords:Feature extraction,Task analysis,Measurement,Semantics,Remote sensing, Image segmentation,Training
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 - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Rösel, Anja
Deposited On:19 Nov 2021 09:25
Last Modified:14 Jan 2022 15:33

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