Abstract
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.
Abstract (translated)
针对高空图像分割的深度神经网络需要大量的标注数据，但高质量的带有精确注释的高空图像数据集稀缺且成本高昂。为了解决这一限制，我们提出了一种自监督预训练方法，该方法可以在减少对标注数据依赖的同时提升分割性能。我们的方法采用基于图像修复（inpainting）的预训练方式，其中模型学习如何重建高空图像中的缺失区域，在捕捉其内在结构后进行道路提取的微调。这种方法提升了泛化能力，增强了领域偏移时的鲁棒性，并且对模型架构和数据集的选择具有不变性。实验结果表明，我们的预训练方法显著提高了分割精度，尤其是在低标注数据的情况下表现尤为出色，使其成为高空图像分析中的可扩展解决方案。
URL
https://arxiv.org/abs/2503.24326
