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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| Published in: | IEEE transactions on geoscience and remote sensing Vol. 60; p. 1 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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New York
IEEE
01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0196-2892, 1558-0644 |
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| Abstract | 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 two types of modality-independent structural relationships in multimodal images. In particular, we present a structural relationship graph representation learning framework for measuring the similarity of the two structural relationships. Firstly, structural graphs are generated from preprocessed multimodal image pairs by means of an object-based image analysis approach. Then, a structural relationship graph convolutional autoencoder (SR-GCAE) is proposed to learn robust and representative features from graphs. Two loss functions aiming at reconstructing vertex information and edge information are presented to make the learned representations applicable for structural relationship similarity measurement. Subsequently, the similarity levels of two structural relationships are calculated from learned graph representations and two difference images are generated based on the similarity levels. After obtaining the difference images, an adaptive fusion strategy is presented to fuse the two difference images. Finally, a morphological filtering-based postprocessing approach is employed to refine the detection results. Experimental results on six datasets with different modal combinations demonstrate the effectiveness of the proposed method. |
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| AbstractList | 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 two types of modality-independent structural relationships in multimodal images. In particular, we present a structural relationship graph representation learning framework for measuring the similarity of the two structural relationships. Firstly, structural graphs are generated from preprocessed multimodal image pairs by means of an object-based image analysis approach. Then, a structural relationship graph convolutional autoencoder (SR-GCAE) is proposed to learn robust and representative features from graphs. Two loss functions aiming at reconstructing vertex information and edge information are presented to make the learned representations applicable for structural relationship similarity measurement. Subsequently, the similarity levels of two structural relationships are calculated from learned graph representations and two difference images are generated based on the similarity levels. After obtaining the difference images, an adaptive fusion strategy is presented to fuse the two difference images. Finally, a morphological filtering-based postprocessing approach is employed to refine the detection results. Experimental results on six datasets with different modal combinations demonstrate the effectiveness of the proposed method. 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 two types of modality-independent structural relationships in multimodal images. In particular, we present a structural relationship graph representation learning framework for measuring the similarity of the two structural relationships. First, structural graphs are generated from preprocessed multimodal image pairs by means of an object-based image analysis approach. Then, a structural relationship graph convolutional autoencoder (SR-GCAE) is proposed to learn robust and representative features from graphs. Two loss functions aiming at reconstructing vertex information and edge information are presented to make the learned representations applicable for structural relationship similarity measurement. Subsequently, the similarity levels of two structural relationships are calculated from learned graph representations, and two difference images are generated based on the similarity levels. After obtaining the difference images, an adaptive fusion strategy is presented to fuse the two difference images. Finally, a morphological filtering-based postprocessing approach is employed to refine the detection results. Experimental results on six datasets with different modal combinations demonstrate the effectiveness of the proposed method. |
| Author | Chen, Hongruixuan Yokoya, Naoto Du, Bo Wu, Chen |
| Author_xml | – sequence: 1 givenname: Hongruixuan orcidid: 0000-0003-0100-4786 surname: Chen fullname: Chen, Hongruixuan organization: Graduate School of Frontier Sciences, University of Tokyo, Chiba, Japan – sequence: 2 givenname: Naoto orcidid: 0000-0002-7321-4590 surname: Yokoya fullname: Yokoya, Naoto organization: Graduate School of Frontier Sciences, University of Tokyo, Chiba, Japan – sequence: 3 givenname: Chen orcidid: 0000-0001-6461-8377 surname: Wu fullname: Wu, Chen organization: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P.R. China – sequence: 4 givenname: Bo surname: Du fullname: Du, Bo organization: School of Computer Science, and Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, P.R. China |
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| References | ref13 ref57 ref12 ref56 ref15 ref59 ref14 ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 ref51 ref50 ref46 ref45 ref48 Wu (ref70) ref47 ref42 ref86 ref41 ref85 ref44 ref88 ref43 ref87 ref49 Baatz (ref76) ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref82 ref81 ref40 ref84 ref83 ref80 ref35 ref79 ref34 ref78 ref37 ref36 ref31 ref75 ref30 ref74 ref33 ref77 ref32 ref2 ref1 ref39 ref38 Velickovic (ref69) 2017 ref71 ref73 ref72 ref24 ref23 ref26 ref25 ref20 ref64 Defferrard (ref68); 29 ref63 ref22 ref66 Kipf (ref67) 2016 ref21 ref65 ref28 ref27 ref29 ref60 ref62 ref61 |
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| SubjectTerms | 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 |
| Title | Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning |
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