# Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection

arXiv: https://arxiv.org/abs/2302.05109 · HTML: https://mlsanity.com/paper/2302.05109/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 98 references that are in the map.
- First posted on arXiv 10 Feb 2023.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [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)
- [Video Classification with Channel-Separated Convolutional Networks](https://mlsanity.com/paper/1904.02811/) (2019)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [General Multi-label Image Classification with Transformers](https://mlsanity.com/paper/2011.14027/) (2020)
- [Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation](https://mlsanity.com/paper/2111.01236/) (2021)
- [Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model](https://mlsanity.com/paper/1909.07726/) (2019)
- [DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images](https://mlsanity.com/paper/2003.03608/) (2020)
- [CPTR: Full Transformer Network for Image Captioning](https://mlsanity.com/paper/2101.10804/) (2021)
- [FCCDN: Feature Constraint Network for VHR Image Change Detection](https://mlsanity.com/paper/2105.10860/) (2021)
- [A Robust Multimodal Remote Sensing Image Registration Method and System Using Steerable Filters with First- and Second-order Gradients](https://mlsanity.com/paper/2202.13347/) (2022)
- [R2FD2: Fast and Robust Matching of Multimodal Remote Sensing Image via Repeatable Feature Detector and Rotation-invariant Feature Descriptor](https://mlsanity.com/paper/2212.02277/) (2022)

## Built on by (most cited first, 13 of 13)

- [MineNetCD: A Benchmark for Global Mining Change Detection on Remote Sensing Imagery](https://mlsanity.com/paper/2407.03971/) (2024)
- [MS-Former: Memory-Supported Transformer for Weakly Supervised Change Detection with Patch-Level Annotations](https://mlsanity.com/paper/2311.09726/) (2023)
- [CDXLSTM: Boosting Remote Sensing Change Detection with Extended Long Short-Term Memory](https://mlsanity.com/paper/2411.07863/) (2024)
- [3D-SSM: A Novel 3D Selective Scan Module for Remote Sensing Change Detection](https://mlsanity.com/paper/2506.19263/) (2025)
- [Non-Registration Change Detection: A Novel Change Detection Task and Benchmark Dataset](https://mlsanity.com/paper/2505.09939/) (2025)
- [Towards Remote Sensing Change Detection with Neural Memory](https://mlsanity.com/paper/2602.10491/) (2026)
- [Bi-temporal Gaussian Feature Dependency Guided Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2410.09539/) (2024)
- [CFNet: Optimizing Remote Sensing Change Detection through Content-Aware Enhancement](https://mlsanity.com/paper/2503.08505/) (2025)
- [UniRSCD: A Unified Novel Architectural Paradigm for Remote Sensing Change Detection](https://mlsanity.com/paper/2511.17930/) (2025)
- [Exchange Is All You Need for Remote Sensing Change Detection](https://mlsanity.com/paper/2601.07805/) (2026)
- [DPG-CD: Depth-Prior-Guided Cross-Modal Joint 2D-3D Change Detection](https://mlsanity.com/paper/2605.07151/) (2026)
- [Building Change Detection in Earthquake: A Multi-Scale Interaction Network and A Change Detection Dataset](https://mlsanity.com/paper/2606.10329/) (2026)
- [JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering](https://mlsanity.com/paper/2606.31745/) (2026)

## Abstract (arXiv)

Deep learning-based change detection (CD) using remote sensing images has received increasing attention in recent years. However, how to effectively extract and fuse the deep features of bi-temporal images for improving the accuracy of CD is still a challenge. To address that, a novel adjacent-level feature fusion network with 3D convolution (named AFCF3D-Net) is proposed in this article. First, through the inner fusion property of 3D convolution, we design a new feature fusion way that can simultaneously extract and fuse the feature information from bi-temporal images. Then, to alleviate the semantic gap between low-level features and high-level features, we propose an adjacent-level feature cross-fusion (AFCF) module to aggregate complementary feature information between the adjacent levels. Furthermore, the full-scale skip connection strategy is introduced to improve the capability of pixel-wise prediction and the compactness of changed objects in the results. Finally, the proposed AFCF3D-Net has been validated on the three challenging remote sensing CD datasets: the Wuhan building dataset (WHU-CD), the LEVIR building dataset (LEVIR-CD), and the Sun Yat-Sen University dataset (SYSU-CD). The results of quantitative analysis and qualitative comparison demonstrate that the proposed AFCF3D-Net achieves better performance compared to other state-of-the-art methods. The code for this work is available at https://github.com/wm-Githuber/AFCF3D-Net.