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Open AccessArticle

Road Extraction from High-Resolution Remote Sensing Imagery Using Refined Deep Residual Convolutional Neural Network

1
School of Geomatics, Liaoning Technical University, Fuxin 123000, China
2
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(5), 552; https://doi.org/10.3390/rs11050552
Received: 19 December 2018 / Revised: 25 February 2019 / Accepted: 28 February 2019 / Published: 6 March 2019
(This article belongs to the Special Issue Convolutional Neural Networks Applications in Remote Sensing)
Road extraction is one of the most significant tasks for modern transportation systems. This task is normally difficult due to complex backgrounds such as rural roads that have heterogeneous appearances with large intraclass and low interclass variations and urban roads that are covered by vehicles, pedestrians and the shadows of surrounding trees or buildings. In this paper, we propose a novel method for extracting roads from optical satellite images using a refined deep residual convolutional neural network (RDRCNN) with a postprocessing stage. RDRCNN consists of a residual connected unit (RCU) and a dilated perception unit (DPU). The RDRCNN structure is symmetric to generate the outputs of the same size. A math morphology and a tensor voting algorithm are used to improve RDRCNN performance during postprocessing. Experiments are conducted on two datasets of high-resolution images to demonstrate the performance of the proposed network architectures, and the results of the proposed architectures are compared with those of other network architectures. The results demonstrate the effective performance of the proposed method for extracting roads from a complex scene. View Full-Text
Keywords: refined deep residual convolutional neural network; road extraction; remote sensing; tensor voting; math morphology; high-resolution imagery refined deep residual convolutional neural network; road extraction; remote sensing; tensor voting; math morphology; high-resolution imagery
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MDPI and ACS Style

Gao, L.; Song, W.; Dai, J.; Chen, Y. Road Extraction from High-Resolution Remote Sensing Imagery Using Refined Deep Residual Convolutional Neural Network. Remote Sens. 2019, 11, 552.

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