Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 21; p. 2499
• Main Authors: Xin, Jiang, Zhang, Xinchang, Zhang, Zhiqiang, Fang, Wu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25.10.2019
• Subjects: Algorithms; artificial intelligence; Automation; buildings; Computer applications; data collection; denseunet; Disaster management; Emergency preparedness; Emergency response; Emergency vehicles; High resolution; high-resolution remote sensing imagery; Image resolution; Image segmentation; Intelligent transportation systems; Learning algorithms; Machine learning; Methods; multi-scale; Neural networks; Parameter robustness; Remote sensing; road extraction; roads; Roads & highways; Semantics; Skips; Transportation networks; Urban areas
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11212499

Road network extraction is one of the significant assignments for disaster emergency response, intelligent transportation systems, and real-time updating road network. Road extraction base on high-resolution remote sensing images has become a hot topic. Presently, most of the researches are based on traditional machine learning algorithms, which are complex and computational because of impervious surfaces such as roads and buildings that are discernible in the images. Given the above problems, we propose a new method to extract the road network from remote sensing images using a DenseUNet model with few parameters and robust characteristics. DenseUNet consists of dense connection units and skips connections, which strengthens the fusion of different scales by connections at various network layers. The performance of the advanced method is validated on two datasets of high-resolution images by comparison with three classical semantic segmentation methods. The experimental results show that the method can be used for road extraction in complex scenes.