A Novel Global-Aware Deep Network for Road Detection of Very High Resolution Remote Sensing Imagery

Abstract

Road detection from very high-resolution (VHR) remote sensing imagery has great importance in a broad array of applications. However, the most advanced deep learning-based methods often produce fragmented road segments, due to the complex backgrounds of images, such as the occlusions and shadows caused by the trees and buildings, or the surrounding objects with similar textures. In this paper, the characteristics of existing models were analyzed and an effective road recognition method was explored, we found that capturing long-range dependencies helps improve road recognition. Therefore, a novel global-aware deep network (GAN) for road detection is proposed, in which the spatial-aware module (SAM) was applied to capture spatial context dependencies and the channel-aware module (CAM) was applied to capture the interchannel dependencies. Through establishing the relationships between spatial contexts and between channels, the GAN could effectively alleviate the road recognition problems, and the advantages of the proposed approach were validated on the public DeepGlobe road dataset. The experimental result demonstrates the superiority of our method.