# C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images

arXiv: https://arxiv.org/abs/2404.13838 · HTML: https://mlsanity.com/paper/2404.13838/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 22 Apr 2024.

## Builds on (most cited first, 18 of 18)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2208.09910/) (2022)
- [Adversarial Learning for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/1802.07934/) (2018)
- [Semi-Supervised Semantic Segmentation with High- and Low-level Consistency](https://mlsanity.com/paper/1908.05724/) (2019)
- [An Empirical Study of Remote Sensing Pretraining](https://mlsanity.com/paper/2204.02825/) (2022)
- [EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2310.19288/) (2023)
- [Fully Convolutional Change Detection Framework with Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection](https://mlsanity.com/paper/2201.06030/) (2022)
- [Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2204.08454/) (2022)
- [HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images](https://mlsanity.com/paper/2404.09178/) (2024)
- [Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2404.09179/) (2024)
- [AI Security for Geoscience and Remote Sensing: Challenges and Future Trends](https://mlsanity.com/paper/2212.09360/) (2022)
- [Binary Change Guided Hyperspectral Multiclass Change Detection](https://mlsanity.com/paper/2112.04493/) (2021)
- [HCGMNET: A Hierarchical Change Guiding Map Network For Change Detection](https://mlsanity.com/paper/2302.10420/) (2023)

## Built on by (most cited first, 7 of 7)

- [HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model](https://mlsanity.com/paper/2406.11519/) (2024)
- [Enhancing Perception of Key Changes in Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2409.12612/) (2024)
- [A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges](https://mlsanity.com/paper/2502.02835/) (2025)
- [SChanger: Change Detection from a Semantic Change and Spatial Consistency Perspective](https://mlsanity.com/paper/2503.20734/) (2025)
- [CGEarthEye:A High-Resolution Remote Sensing Vision Foundation Model Based on the Jilin-1 Satellite Constellation](https://mlsanity.com/paper/2507.00356/) (2025)
- [DAM-Net: Domain Adaptation Network with Micro-Labeled Fine-Tuning for Change Detection](https://mlsanity.com/paper/2504.13748/) (2025)
- [HSANET: A Hybrid Self-Cross Attention Network For Remote Sensing Change Detection](https://mlsanity.com/paper/2504.15170/) (2025)

## Abstract (arXiv)

A high-precision feature extraction model is crucial for change detection (CD). In the past, many deep learning-based supervised CD methods learned to recognize change feature patterns from a large number of labelled bi-temporal images, whereas labelling bi-temporal remote sensing images is very expensive and often time-consuming; therefore, we propose a coarse-to-fine semi-supervised CD method based on consistency regularization (C2F-SemiCD), which includes a coarse-to-fine CD network with a multiscale attention mechanism (C2FNet) and a semi-supervised update method. Among them, the C2FNet network gradually completes the extraction of change features from coarse-grained to fine-grained through multiscale feature fusion, channel attention mechanism, spatial attention mechanism, global context module, feature refine module, initial aggregation module, and final aggregation module. The semi-supervised update method uses the mean teacher method. The parameters of the student model are updated to the parameters of the teacher Model by using the exponential moving average (EMA) method. Through extensive experiments on three datasets and meticulous ablation studies, including crossover experiments across datasets, we verify the significant effectiveness and efficiency of the proposed C2F-SemiCD method. The code will be open at: https://github.com/ChengxiHAN/C2F-SemiCDand-C2FNet.