wchao0601/m4-sar

M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for Optical-SAR Fusion Object Detection

2025

Dataset description

Single-source remote sensing object detection using optical or SAR images struggles in complex environments. Optical images offer rich textural details but are often affected by low-light, cloud-obscured, or low-resolution conditions, reducing the detection performance. SAR images are robust to weather, but suffer from speckle noise and limited semantic expressiveness. Optical and SAR images provide complementary advantages, and fusing them can significantly improve the detection accuracy. However, progress in this field is hindered by the lack of large-scale, standardized datasets. To address these challenges, we propose the first comprehensive dataset for optical-SAR fusion object detection, named \textbf{M}ulti-resolution, \textbf{M}ulti-polarization, \textbf{M}ulti-scene, \textbf{M}ulti-source \textbf{SAR} dataset (\textbf{M4-SAR}). It contains 112,184 precisely aligned image pairs and nearly one million labeled instances with arbitrary orientations, spanning six key categories. To enable standardized evaluation, we develop a unified benchmarking toolkit that integrates six state-of-the-art multi-source fusion methods. Furthermore, we propose E2E-OSDet, a novel end-to-end multi-source fusion detection framework that mitigates cross-domain discrepancies and establishes a robust baseline for future studies. Extensive experiments on M4-SAR demonstrate that fusing optical and SAR data can improve $mAP$ by 5.7% over single-source inputs, with particularly significant gains in complex environments. The dataset and code are publicly available at link.

Contact

If you have any questions, please feel free to contact me via email at [email protected]

Citation

If our work is helpful, you can cite our paper:

@article{wang2025m4,
  title={M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for Optical-SAR Fusion Object Detection},
  author={Wang, Chao and Lu, Wei and Li, Xiang and Yang, Jian and Luo, Lei},
  journal={arXiv preprint arXiv:2505.10931},
  year={2025}
}