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.
Highlights
Dozens of Hough transform (HT) extensions have been developed for solving straight line road detection problem
We propose a new method based on a generalized HT (i.e., Radon transform) and apply it for straight road detection in remote sensing images
We Infarmland, this section, we demonstrate someregion, experimental results of test samples and illustrate how our selected remote sensing images with a straight line road to verify the proposed algorithm, and method is superior to the traditional algorithms in terms of accuracy and computing complexity
Summary
The determination of the location and orientation of a straight line road is a fundamental task for many computer vision applications such as road network extraction [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23], image registration [4],visual tracking [5], robot autonomous navigation [6], hyperspectral image classification [7,8], GlobalNavigation Satellite System(GNSS) [9,10], unmanned aerial vehicle images [11], and sports video broadcasting [12,13]. A Hough transform (HT) [14,15,16] is one of the very typical methods and has been widely applied to computer processing, image processing, and digital image processing. It transforms the problem of a global detection in a binary image into peaks detection in a Hough parameter space. Dozens of HT extensions have been developed for solving straight line road detection problem. These methods can be divided into the following four groups: generalized. HT (GHT) [17,18,19,20,21], randomized HT (RHT) [22,23,24,25], probabilistic HT (PHT) [26,27,28,29], and fuzzy HT (FHT) [30,31,32].
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