# RFL-CDNet: Towards Accurate Change Detection via Richer Feature Learning

arXiv: https://arxiv.org/abs/2404.17765 · HTML: https://mlsanity.com/paper/2404.17765/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 39 references that are in the map.
- First posted on arXiv 27 Apr 2024.

## Builds on (most cited first, 21 of 21)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [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)
- [Richer Convolutional Features for Edge Detection](https://mlsanity.com/paper/1612.02103/) (2016)
- [DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images](https://mlsanity.com/paper/2003.03608/) (2020)
- [How to Reduce Change Detection to Semantic Segmentation](https://mlsanity.com/paper/2206.07557/) (2022)
- [An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection](https://mlsanity.com/paper/2204.00154/) (2022)
- [Fully Transformer Network for Change Detection of Remote Sensing Images](https://mlsanity.com/paper/2210.00757/) (2022)
- [TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images](https://mlsanity.com/paper/2310.14214/) (2023)
- [VcT: Visual change Transformer for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2310.11417/) (2023)
- [T-UNet: Triplet UNet for Change Detection in High-Resolution Remote Sensing Images](https://mlsanity.com/paper/2308.02356/) (2023)

## Built on by (most cited first, 6 of 6)

- [UniChange: Unifying Change Detection with Multimodal Large Language Model](https://mlsanity.com/paper/2511.02607/) (2025)
- [Treat Stillness with Movement: Remote Sensing Change Detection via Coarse-grained Temporal Foregrounds Mining](https://mlsanity.com/paper/2408.08078/) (2024)
- [Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection](https://mlsanity.com/paper/2412.16918/) (2024)
- [Graph Aggregation Prototype Learning for Semantic Change Detection in Remote Sensing](https://mlsanity.com/paper/2507.10938/) (2025)
- [TERDNet: Transformer Encoder-Recurrent Decoder Network for Scene Change Detection](https://mlsanity.com/paper/2605.20822/) (2026)
- [iVISION-2DCD: A Long-Term Change Detection Dataset for Large-Scale Outdoor Construction Monitoring](https://mlsanity.com/paper/2607.03553/) (2026)

## Abstract (arXiv)

Change Detection is a crucial but extremely challenging task of remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However, most existing deep learning-based change detection methods mainly focus on intricate feature extraction and multi-scale feature fusion, while ignoring the insufficient utilization of features in the intermediate stages, thus resulting in sub-optimal results. To this end, we propose a novel framework, named RFL-CDNet, that utilizes richer feature learning to boost change detection performance. Specifically, we first introduce deep multiple supervision to enhance intermediate representations, thus unleashing the potential of backbone feature extractor at each stage. Furthermore, we design the Coarse-To-Fine Guiding (C2FG) module and the Learnable Fusion (LF) module to further improve feature learning and obtain more discriminative feature representations. The C2FG module aims to seamlessly integrate the side prediction from the previous coarse-scale into the current fine-scale prediction in a coarse-to-fine manner, while LF module assumes that the contribution of each stage and each spatial location is independent, thus designing a learnable module to fuse multiple predictions. Experiments on several benchmark datasets show that our proposed RFL-CDNet achieves state-of-the-art performance on WHU cultivated land dataset and CDD dataset, and the second-best performance on WHU building dataset. The source code and models are publicly available at https://github.com/Hhaizee/RFL-CDNet.