Road detection based on convolutional neural networks (CNNs) has achieved remarkable performances for very high resolution (VHR) remote sensing images. However, this approach relies on massive annotated samples, and the problem of limited generalization for unseen images still remains. The manual pixel-level labeling process is also extremely time-consuming, and the performance of CNNs degrades significantly when there is a domain gap between the training and test images. In this paper, to address this problem, a global-local adversarial learning (GOAL) framework is proposed for cross-domain road detection. On the one hand, considering the spatial information similarities between the source and target domains, feature space driven adversarial learning is applied to explore the shared features across domains. On the other hand, the complex background of VHR remote sensing images, such as the occlusions and shadows of trees and buildings, makes some roads easy to recognize, while others are much more difficult. However, the traditional global adversarial learning approach cannot guarantee local semantic consistency. Therefore, a local alignment operation is introduced, which adaptively adjusts the weight of the adversarial loss according to the road recognition difficulty. Extensive experiments were conducted on different road datasets, including two public competition road datasets—SpaceNet and DeepGlobe—and our own large-scale annotated images from four cities: Boston, Birmingham, Shanghai, and Wuhan. The experimental results show that the proposed GOAL framework can clearly improve the cross-domain road detection performance, without any annotation of the target domain images. For instance, taking SpaceNet road dataset as the source domain, compared with the no adaptation method, the IOU performance of GOAL framework is increased by 14.36%, 5.49%, 4.51%, 5.63% and 15.14% on DeepGlobe, Boston, Birmingham, Shanghai, and Wuhan images, respectively, which demonstrates its strong generalization capability.

基于卷积神经网络 (CNN) 的道路检测在超高分辨率 (VHR) 遥感图像方面取得了卓越的性能。然而，这种方法依赖于大量的标注样本，对于看不见的图像的泛化能力有限的问题仍然存在。手动像素级标记过程也非常耗时，当训练和测试图像之间存在域间隙时，CNN 的性能会显着下降。在本文中，为了解决这个问题，提出了一种用于跨域道路检测的全局-局部对抗学习（GOAL）框架。一方面，考虑到源域和目标域之间的空间信息相似性，特征空间驱动的对抗学习被应用于探索跨域的共享特征。另一方面，VHR 遥感图像的复杂背景，例如树木和建筑物的遮挡和阴影，使某些道路容易识别，而其他道路则要困难得多。然而，传统的全局对抗学习方法不能保证局部语义一致性。因此，引入了局部对齐操作，根据道路识别难度自适应调整对抗性损失的权重。我们对不同的道路数据集进行了广泛的实验，包括两个公共竞赛道路数据集——SpaceNet 和 DeepGlobe——以及我们自己的来自四个城市的大规模注释图像：波士顿、伯明翰、上海和武汉。实验结果表明，提出的GOAL框架可以明显提高跨域道路检测性能，没有任何目标域图像的注释。例如，以SpaceNet道路数据集为源域，在DeepGlobe，波士顿，伯明翰，上海，与无自适应方法相比，GOAL框架的IOU性能分别提高了14.36%、5.49%、4.51%、5.63%和15.14%和武汉图像，显示了其强大的泛化能力。