A Nov Al Global-Local Adversarial Network for Unsupervised Cross-Domain Road Detection

Road detection based on convolutional neural networks (CNNs) has achieved remarkable performances for very high resolution (VHR) remote sensing images. However, it relies on a large number of labeled samples and the problem of limited generalization for unseen images still remains. The manual pixel-level labeling process is extremely time-consuming, and the performance of CNN s degrades significantly when there is a domain gap between the training and test images. To address this problem, a global-local adversarial network (GLANet) is proposed for unsupervised 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 not. However, the traditional global adversarial learning approach cannot guarantee local semantic consistency. Therefore, a local alignment operation, which adaptively adjusts the weight of the adversarial loss according to the road recognition difficulty, is introduced. The experiments conducted on public road datasets show that the proposed method can effectively improve the cross-domain road detection performance, which demonstrates its strong generalization ability.