Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning

Unsupervised multimodal change detection is a practical and challenging topic that can play an important role in time-sensitive emergency applications. To address the challenge that multimodal remote sensing images cannot be directly compared due to their modal heterogeneity, we take advantage of tw...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 60; S. 1
Hauptverfasser:	Chen, Hongruixuan, Yokoya, Naoto, Wu, Chen, Du, Bo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Change detection Detection Feature extraction graph convolutional autoencoder graph representation learning Graph representations Graphical representations Graphs Heterogeneity Image analysis Image filters Image processing Image sensors Learning multimodal remote sensing images Optical imaging Optical sensors Radar polarimetry Remote sensing Representation learning Similarity structural relationship
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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