# CFNet: Optimizing Remote Sensing Change Detection through Content-Aware Enhancement

arXiv: https://arxiv.org/abs/2503.08505 · HTML: https://mlsanity.com/paper/2503.08505/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [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)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [Style Transfer by Relaxed Optimal Transport and Self-Similarity](https://mlsanity.com/paper/1904.12785/) (2019)
- [Changer: Feature Interaction is What You Need for Change Detection](https://mlsanity.com/paper/2209.08290/) (2022)
- [Arbitrary Style Transfer with Deep Feature Reshuffle](https://mlsanity.com/paper/1805.04103/) (2018)
- [Separating Style and Content for Generalized Style Transfer](https://mlsanity.com/paper/1711.06454/) (2017)
- [Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review](https://mlsanity.com/paper/2305.05813/) (2023)
- [Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2404.09179/) (2024)
- [HCGMNET: A Hierarchical Change Guiding Map Network For Change Detection](https://mlsanity.com/paper/2302.10420/) (2023)
- [Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2302.05109/) (2023)
- [A Unified Framework for Generalizable Style Transfer: Style and Content Separation](https://mlsanity.com/paper/1806.05173/) (2018)
- [Curriculum-style Local-to-global Adaptation for Cross-domain Remote Sensing Image Segmentation](https://mlsanity.com/paper/2203.01539/) (2022)
- [EfficientCD: A New Strategy For Change Detection Based With Bi-temporal Layers Exchanged](https://mlsanity.com/paper/2407.15999/) (2024)

## Abstract (arXiv)

Change detection is a crucial and widely applied task in remote sensing, aimed at identifying and analyzing changes occurring in the same geographical area over time. Due to variability in acquisition conditions, bi-temporal remote sensing images often exhibit significant differences in image style. Even with the powerful generalization capabilities of DNNs, these unpredictable style variations between bi-temporal images inevitably affect model's ability to accurately detect changed areas. To address issue above, we propose the Content Focuser Network (CFNet), which takes content-aware strategy as a key insight. CFNet employs EfficientNet-B5 as the backbone for feature extraction. To enhance the model's focus on the content features of images while mitigating the misleading effects of style features, we develop a constraint strategy that prioritizes the content features of bi-temporal images, termed Content-Aware. Furthermore, to enable the model to flexibly focus on changed and unchanged areas according to the requirements of different stages, we design a reweighting module based on the cosine distance between bi-temporal image features, termed Focuser. CFNet achieve outstanding performance across 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 code and pretrained models of CFNet are publicly released at https://github.com/wifiBlack/CFNet.