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Fully Convolutional Siamese Networks for Change Detection

arXiv 1810.08462 ↗First posted 19 Oct 2018cs.CV cs.LGcited by 305

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Abstract (arXiv)

This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images. Most notably, we propose two Siamese extensions of fully convolutional networks which use heuristics about the current problem to achieve the best results in our tests on two open change detection datasets, using both RGB and multispectral images. We show that our system is able to learn from scratch using annotated change detection images. Our architectures achieve better performance than previously proposed methods, while being at least 500 times faster than related systems. This work is a step towards efficient processing of data from large scale Earth observation systems such as Copernicus or Landsat.

Key facts. Cited by 305 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Oct 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 25; 2022: 30; 2023: 52; 2024: 69; 2025: 64; 2026: 65.

305 papers.

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  1. 2021Remote Sensing Image Change Detection with Transformerscited by 305
  2. 2022A Transformer-Based Siamese Network for Change Detectioncited by 243
  3. 2021Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Datacited by 214
  4. 2023Learning to Exploit Temporal Structure for Biomedical Vision-Language Processingcited by 210
  5. 2024ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Modelcited by 118
  6. 2023Towards Geospatial Foundation Models via Continual Pretrainingcited by 109
  7. 2022An Empirical Study of Remote Sensing Pretrainingcited by 105
  8. 2024RS-Mamba for Large Remote Sensing Image Dense Predictioncited by 80
  9. 2022DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentationcited by 78
  10. 2022Changer: Feature Interaction is What You Need for Change Detectioncited by 78
  11. 2021S2Looking: A Satellite Side-Looking Dataset for Building Change Detectioncited by 77
  12. 2021TorchGeo: Deep Learning With Geospatial Datacited by 73
38 more
  1. 2024HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Modelcited by 72
  2. 2024TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Datacited by 69
  3. 2024MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretrainingcited by 66
  4. 2021Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagerycited by 64
  5. 2024PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Modelscited by 62
  6. 2022Transformers in Remote Sensing: A Surveycited by 61
  7. 2024Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysiscited by 60
  8. 2023Adapting Segment Anything Model for Change Detection in HR Remote Sensing Imagescited by 56
  9. 2021Self-Supervised Material and Texture Representation Learning for Remote Sensing Taskscited by 49
  10. 2024CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detectioncited by 48
  11. 2021Describing and Localizing Multiple Changes with Transformerscited by 47
  12. 2023A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detectioncited by 47
  13. 2022Fully Convolutional Change Detection Framework with Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detectioncited by 46
  14. 2024LSKNet: A Foundation Lightweight Backbone for Remote Sensingcited by 44
  15. 2021Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Imagescited by 43
  16. 2022Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Imagescited by 43
  17. 2023Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Reviewcited by 41
  18. 2024Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Modelcited by 37
  19. 2022TINYCD: A (Not So) Deep Learning Model For Change Detectioncited by 35
  20. 2024Segment Any Changecited by 35
  21. 2021DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detectioncited by 32
  22. 2022Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Imagescited by 30
  23. 2024HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Imagescited by 30
  24. 2024Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagerycited by 30
  25. 2022How to Reduce Change Detection to Semantic Segmentationcited by 28
  26. 2024Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectivescited by 27
  27. 2023Time Travelling Pixels: Bitemporal Features Integration with Foundation Model for Remote Sensing Image Change Detectioncited by 26
  28. 2021FCCDN: Feature Constraint Network for VHR Image Change Detectioncited by 25
  29. 20223D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphscited by 24
  30. 2021Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutionscited by 23
  31. 2023Remote Sensing Change Detection With Transformers Trained from Scratchcited by 22
  32. 2023Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Processcited by 22
  33. 2021Self-supervised pre-training enhances change detection in Sentinel-2 imagerycited by 21
  34. 2023GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature Guided DDPMcited by 21
  35. 2023Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localizationcited by 21
  36. 2023Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchangecited by 20
  37. 2021BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Imagescited by 19
  38. 2022DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detectioncited by 19

50 most cited of 305 All 305 (JSON)