用自监督方法提升航拍道路提取，减少对标注数据的依赖。
Self-Supervised Pretraining for Aerial Road Extraction
- 通过图像修复预训练，让模型学习航拍图结构
- 低数据场景下分割准确率显著提升
- 适配多种模型和数据集，通用性强
航拍图像分割的深度神经网络需要大量标注数据，但高质量的航拍数据集标注成本高、资源少。为此，我们提出一种自监督预训练方法，可在减少标注依赖的同时提升分割性能。该方法基于图像修复机制，使模型学习重建航拍图像中缺失区域的结构，从而在微调至道路提取任务前掌握图像内在规律。实验表明，该预训练方法能显著提升分割精度，尤其在数据稀缺条件下表现优异，具备良好的泛化能力与域适应性，且不依赖特定模型架构或数据集，为航拍图像分析提供可扩展的解决方案。
原文摘要 · Abstract (English)
Deep neural networks for aerial image segmentation require large amounts of labeled data, but high-quality aerial datasets with precise annotations are scarce and costly to produce. To address this limitation, we propose a self-supervised pretraining method that improves segmentation performance while reducing reliance on labeled data. Our approach uses inpainting-based pretraining, where the model learns to reconstruct missing regions in aerial images, capturing their inherent structure before being fine-tuned for road extraction. This method improves generalization, enhances robustness to domain shifts, and is invariant to model architecture and dataset choice. Experiments show that our pretraining significantly boosts segmentation accuracy, especially in low-data regimes, making it a scalable solution for aerial image analysis.
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