mlsanityData to 7 Oct 2026

Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning

arXiv 2210.00941 ↗First posted 3 Oct 2022cs.CV eess.IV eess.SPcited by 12

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Abstract (arXiv)

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 five datasets with different modal combinations demonstrate the effectiveness of the proposed method.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 22 of its 87 references that are in the map. First posted on arXiv 3 Oct 2022.

Builds on

22 of 87 references.

References · 22

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  11. 2019Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Networkcited by 24
  12. 2020Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detectioncited by 22
10 more
  1. 2019Unsupervised Image Regression for Heterogeneous Change Detectioncited by 21
  2. 2022Dual-Tasks Siamese Transformer Framework for Building Damage Assessmentcited by 19
  3. 2020Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Imagescited by 17
  4. 2021A Multiscale Graph Convolutional Network for Change Detection in Homogeneous and Heterogeneous Remote Sensing Imagescited by 12
  5. 2020Hyperspectral Anomaly Change Detection Based on Auto-encodercited by 11
  6. 2019Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Networkscited by 9
  7. 2020Change Detection in Heterogeneous Optical and SAR Remote Sensing Images via Deep Homogeneous Feature Fusioncited by 9
  8. 2022Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Networkcited by 6
  9. 2020Adaptive Local Structure Consistency based Heterogeneous Remote Sensing Change Detectioncited by 4
  10. 2020An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Imagescited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 4; 2025: 1; 2026: 1.

12 papers.

  1. 2024ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Modelcited by 118
  2. 2024Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagerycited by 30
  3. 2023Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchangecited by 20
  4. 2024Change Detection Between Optical Remote Sensing Imagery and Map Data via Segment Anything Model (SAM)cited by 12
  5. 2023ObjFormer: Learning Land-Cover Changes From Paired OSM Data and Optical High-Resolution Imagery via Object-Guided Transformercited by 8
  6. 2023T-UNet: Triplet UNet for Change Detection in High-Resolution Remote Sensing Imagescited by 4
  7. 2023Transformer-based Multimodal Change Detection with Multitask Consistency Constraintscited by 4
  8. 2023NN-Copula-CD: A Copula-Guided Interpretable Neural Network for Change Detection in Heterogeneous Remote Sensing Imagescited by 3
  9. 2023EMS-Net: Efficient Multi-Temporal Self-Attention For Hyperspectral Change Detectioncited by 2
  10. 2025S2C: Learning Noise-Resistant Differences for Unsupervised Change Detection in Multimodal Remote Sensing Imagescited by 2
  11. 2024Quantum Information-Empowered Graph Neural Network for Hyperspectral Change Detectioncited by 1
  12. 2026ChangeQuery: Advancing Remote Sensing Change Analysis for Natural and Human-Induced Disasters from Visual Detection to Semantic Understandingcited by 0