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".
🐣 News
- [2025.3.11] We release the code and checkpoints for CFNet 🚀
- [2025.3.11] We release the arixv paper 🚀
🤔 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.
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%). 🚀
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.
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.
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).
🏁 Get Start
Code: https://github.com/wifiBlack/CFNet
Installation
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