Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network

计算机科学 人工智能 卷积神经网络 特征提取 边距(机器学习) 像素 端到端原则 计算机视觉 人工神经网络 模式识别(心理学) 代表(政治) 深度学习 任务(项目管理) 特征(语言学) 遥感 机器学习 地质学 哲学 经济 管理 法学 政治 语言学 政治学
作者
Guangliang Cheng,Ying Wang,Shibiao Xu,Hongzhen Wang,Shiming Xiang,Chunhong Pan
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:55 (6): 3322-3337 被引量:409
标识
DOI:10.1109/tgrs.2017.2669341
摘要

Accurate road detection and centerline extraction from very high resolution (VHR) remote sensing imagery are of central importance in a wide range of applications. Due to the complex backgrounds and occlusions of trees and cars, most road detection methods bring in the heterogeneous segments; besides for the centerline extraction task, most current approaches fail to extract a wonderful centerline network that appears smooth, complete, as well as single-pixel width. To address the above-mentioned complex issues, we propose a novel deep model, i.e., a cascaded end-to-end convolutional neural network (CasNet), to simultaneously cope with the road detection and centerline extraction tasks. Specifically, CasNet consists of two networks. One aims at the road detection task, whose strong representation ability is well able to tackle the complex backgrounds and occlusions of trees and cars. The other is cascaded to the former one, making full use of the feature maps produced formerly, to obtain the good centerline extraction. Finally, a thinning algorithm is proposed to obtain smooth, complete, and single-pixel width road centerline network. Extensive experiments demonstrate that CasNet outperforms the state-of-the-art methods greatly in learning quality and learning speed. That is, CasNet exceeds the comparing methods by a large margin in quantitative performance, and it is nearly 25 times faster than the comparing methods. Moreover, as another contribution, a large and challenging road centerline data set for the VHR remote sensing image will be publicly available for further studies.
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