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

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...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2019-11, Vol.11 (21), p.2499
Main Authors: Xin, Jiang, Zhang, Xinchang, Zhang, Zhiqiang, Fang, Wu
Format: Article
Language:English
Subjects:
Algorithms
Automation
Computer applications
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
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs11212499