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Open AccessArticle

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

1
Department of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
2
School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
3
College of Surveying and Geo-informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
4
College of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2499; https://doi.org/10.3390/rs11212499
Received: 25 September 2019 / Revised: 15 October 2019 / Accepted: 20 October 2019 / Published: 25 October 2019
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. View Full-Text
Keywords: high-resolution remote sensing imagery; multi-scale; road extraction; machine learning; DenseUNet high-resolution remote sensing imagery; multi-scale; road extraction; machine learning; DenseUNet
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MDPI and ACS Style

Xin, J.; Zhang, X.; Zhang, Z.; Fang, W. Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet. Remote Sens. 2019, 11, 2499.

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