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Remote Sens. 2017, 9(6), 590; doi:10.3390/rs9060590

Road Detection by Using a Generalized Hough Transform

1
College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China
2
The 16th Institute, China Aerospace Science and Technology Corporation, Xi’an 710100, China
3
School of Information Science and Engineering, Yunnan University, Kunming 650091, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez and Prasad S. Thenkabail
Received: 28 April 2017 / Revised: 4 June 2017 / Accepted: 8 June 2017 / Published: 10 June 2017
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
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Abstract

Road detection plays key roles for remote sensing image analytics. Hough transform (HT) is one very typical method for road detection, especially for straight line road detection. Although many variants of Hough transform have been reported, it is still a great challenge to develop a low computational complexity and time-saving Hough transform algorithm. In this paper, we propose a generalized Hough transform (i.e., Radon transform) implementation for road detection in remote sensing images. Specifically, we present a dictionary learning method to approximate the Radon transform. The proposed approximation method treats a Radon transform as a linear transform, which then facilitates parallel implementation of the Radon transform for multiple images. To evaluate the proposed algorithm, we conduct extensive experiments on the popular RSSCN7 database for straight road detection. The experimental results demonstrate that our method is superior to the traditional algorithms in terms of accuracy and computing complexity. View Full-Text
Keywords: Hough transform; dictionary learning; road detection; Radon transform Hough transform; dictionary learning; road detection; Radon transform
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Liu, W.; Zhang, Z.; Li, S.; Tao, D. Road Detection by Using a Generalized Hough Transform. Remote Sens. 2017, 9, 590.

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