# Synthetic Aperture Radar Image Change Detection Based on Global Dynamic Context-Aware Network

arXiv: https://arxiv.org/abs/2605.16764 · HTML: https://mlsanity.com/paper/2605.16764/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 64 references that are in the map.
- First posted on arXiv 16 May 2026.

## Builds on (most cited first, 20 of 20)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
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- [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019)
- [CondConv: Conditionally Parameterized Convolutions for Efficient Inference](https://mlsanity.com/paper/1904.04971/) (2019)
- [Agent Attention: On the Integration of Softmax and Linear Attention](https://mlsanity.com/paper/2312.08874/) (2023)
- [AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data Generation](https://mlsanity.com/paper/2411.15497/) (2024)
- [Progressive Focused Transformer for Single Image Super-Resolution](https://mlsanity.com/paper/2503.20337/) (2025)
- [GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion](https://mlsanity.com/paper/2206.02850/) (2022)
- [Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection](https://mlsanity.com/paper/2001.04271/) (2020)
- [From W-Net to CDGAN: Bi-temporal Change Detection via Deep Learning Techniques](https://mlsanity.com/paper/2003.06583/) (2020)
- [SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation](https://mlsanity.com/paper/2001.07645/) (2020)
- [Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2212.10236/) (2022)
- [TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images](https://mlsanity.com/paper/2310.14214/) (2023)
- [Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark](https://mlsanity.com/paper/2301.03432/) (2023)
- [Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network](https://mlsanity.com/paper/2203.06543/) (2022)
- [Convolution and Attention Mixer for Synthetic Aperture Radar Image Change Detection](https://mlsanity.com/paper/2309.12010/) (2023)
- [SAR Image Change Detection Based on Multiscale Capsule Network](https://mlsanity.com/paper/2201.08935/) (2022)
- [Towards Cross-Disaster Building Damage Assessment with Graph Convolutional Networks](https://mlsanity.com/paper/2201.10395/) (2022)
- [Wavelet-based Bi-dimensional Aggregation Network for SAR Image Change Detection](https://mlsanity.com/paper/2407.13151/) (2024)

## Abstract (arXiv)

Convolutional neural networks (CNNs) have been extensively and successfully applied to the task of synthetic aperture radar (SAR) image change detection. However, conventional convolutional layers are inherently limited by their local receptive fields, which mainly capture spatially localized patterns while neglecting the global context that is often crucial for accurately distinguishing subtle or large-scale changes in SAR imagery. To address these limitations, we propose a novel Global Dynamic Context-Aware Network (GDNet) specifically tailored for SAR image change detection. At the core of our approach lies a novel global dynamic convolution module, which adaptively modulates convolution kernel weights according to the global semantic information extracted from the input features. By dynamically incorporating long-range dependencies, this mechanism enables the network to integrate both local detail and global context, thus improving its ability to detect diverse change patterns. In addition, we introduce a carefully designed two-stage Mixup strategy for model training. Unlike conventional single-stage Mixup, our two-stage design generates more diverse and informative training samples, effectively regularizing the model and yielding more stable and reliable classification results even under limited data scenarios. Extensive experiments on three SAR datasets demonstrate the superiority of the proposed GDNet compared to other state-of-the-art methods. These findings highlight the potential of global dynamic modeling and advanced data augmentation strategies for advancing SAR image interpretation. Source codes are available at urlhttps://github.com/oucailab/GDNet.