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| license: apache-2.0 |
| pipeline_tag: image-segmentation |
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| # CFNet: Optimizing Remote Sensing 🗺️ Change Detection 🕵 through Content-Aware Enhancement |
| Official repository for the paper ["CFNet: Optimizing Remote Sensing Change Detection through Content-Aware Enhancement"](https://arxiv.org/pdf/2503.08505). |
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| [[🤗Datasets](https://huggingface.co/datasets/wifibk/CFNet_Datasets/tree/main)] [[🤗Checkpoints](https://huggingface.co/wifibk/CFNet/tree/main)] |
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| ## 🐣 News |
| - **[2025.3.11]** We release the code and checkpoints for CFNet 🚀 |
| - **[2025.3.11]** We release the [arixv paper](https://arxiv.org/pdf/2503.08505) 🚀 |
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| ## 🤔 Addressing Style Variations in Change Detection |
| Change detection plays a crucial role in remote sensing, enabling the identification and analysis of temporal changes in the same geographical area. However, bi-temporal remote sensing images often exhibit significant style variations due to differences in acquisition conditions. These unpredictable variations pose a challenge to deep neural networks (DNNs), affecting their ability to accurately detect changes. |
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| <p align="center"> |
| <img src="figures/Content.png" width="90%"> <br> |
| </p> |
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| To address the problem above, we propose **Content Focuser Network (CFNet)**. CFNet achieves state-of-the-art performance on three well-known change detection datasets: **CLCD (F1: 81.41%, IoU: 68.65%)**, **LEVIR-CD (F1: 92.18%, IoU: 85.49%)**, and **SYSU-CD (F1: 82.89%, IoU: 70.78%)**. 🚀 |
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| <p align="center"> |
| <img src="figures/Architecture.png" width="90%"> <br> |
| </p> |
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| The main contributions of our work: |
| - **Content-Aware strategy**, a novel content-based constraint learning strategy that enhances the model's focus on intrinsic content features while **reducing the impact of style variations**, thereby improving the accuracy and robustness of bi-temporal change detection in remote sensing imagery. |
| - **Focuser module**, a novel mechanism that dynamically reweights features to focus on both changed and unchanged areas, leveraging their **mutual constraints** to enhance parameter regularization and improve model accuracy. |
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| **The visualization results on the CLCD dataset** demonstrate the great performance of CFNet. For better readability, we present only the results from CLCD here. For a comprehensive view, including visualizations on all three datasets, please refer to our paper. |
| <p align="center"> |
| <img src="figures/cl_vis.png" width="90%"> <br> |
| </p> |
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| **To further illustrate the effectiveness of the Content-Aware strategy in extracting content features**, we visualize the largest-scale feature maps output by the Content Decoder. Since the LEVIR-CD dataset primarily focuses on building changes, the content features predominantly represent structural information related to buildings). |
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| <p align="center"> |
| <img src="figures/content_res.png" width="90%"> <br> |
| </p> |
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| ## 🏁 Get Start |
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| Code: https://github.com/wifiBlack/CFNet |
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| ### Installation |
| ... |
