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Semisupervised change detection using Graph Convolutional Network

Saha, Sudipan and Mou, LiChao and Zhu, Xiao Xiang and Bovolo, Francesca and Bruzzone, Lorenzo (2021) Semisupervised change detection using Graph Convolutional Network. IEEE Geoscience and Remote Sensing Letters. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2020.2985340. ISSN 1545-598X. (In Press)

Full text not available from this repository.

Official URL: https://ieeexplore.ieee.org/document/9069898

Abstract

Most change detection (CD) methods are unsupervised as collecting substantial multitemporal training data is challenging. Unsupervised CD methods are driven by heuristics and lack the capability to learn from data. However, in many real-world applications, it is possible to collect a small amount of labeled data scattered across the analyzed scene. Such a few scattered labeled samples in the pool of unlabeled samples can be effectively handled by graph convolutional network (GCN) that has recently shown good performance in semisupervised single-date analysis, to improve change detection performance. Based on this, we propose a semisupervised CD method that encodes multitemporal images as a graph via multiscale parcel segmentation that effectively captures the spatial and spectral aspects of the multitemporal images. The graph is further processed through GCN to learn a multitemporal model. Information from the labeled parcels is propagated to the unlabeled ones over training iterations. By exploiting the homogeneity of the parcels, the model is used to infer the label at a pixel level. To show the effectiveness of the proposed method, we tested it on a multitemporal Very High spatial Resolution (VHR) data set acquired by Pleiades sensor over Trento, Italy.

Item URL in elib:https://elib.dlr.de/140906/
Document Type:Article
Title:Semisupervised change detection using Graph Convolutional Network
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Saha, SudipanUNSPECIFIEDUNSPECIFIED
Mou, LiChaoLiChao.Mou (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Bovolo, Francescabovolo (at) fbk.euUNSPECIFIED
Bruzzone, LorenzoUniversity of TrentoUNSPECIFIED
Date:2021
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1109/LGRS.2020.2985340
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
Status:In Press
Keywords:change detection, semi-supervised, graph convolutional network
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 - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Bratasanu, Ion-Dragos
Deposited On:12 Feb 2021 16:52
Last Modified:12 Feb 2021 16:52

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