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Journal of Applied Remote Sensing • Open Access

Extracting roads based on Retinex and improved Canny operator with shape criteria in vague and unevenly illuminated aerial images
Author(s): Ronggui Ma; Weixing Wang; Sheng Liu

Paper Abstract

An automatic road extraction method for vague aerial images is proposed in this paper. First, a high-resolution but low-contrast image is enhanced by using a Retinex-based algorithm. Then, the enhanced image is segmented with an improved Canny edge detection operator that can automatically threshold the image into a binary edge image. Subsequently, the linear and curved road segments are regulated by the Hough line transform and extracted based on several thresholds of road size and shapes, in which a number of morphological operators are used such as thinning (skeleton), junction detection, and endpoint detection. In experiments, a number of vague aerial images with bad uniformity are selected for testing. Similarity and discontinuation-based algorithms, such as Otsu thresholding, merge and split, edge detection-based algorithms, and the graph-based algorithm are compared with the new method. The experiment and comparison results show that the studied method can enhance vague, low-contrast, and unevenly illuminated color aerial road images; it can detect most road edges with fewer disturb elements and trace roads with good quality. The method in this study is promising.

Paper Details

Date Published: 3 December 2012
PDF: 14 pages
J. Appl. Remote Sens. 6(1) 063610 doi: 10.1117/1.JRS.6.063610
Published in: Journal of Applied Remote Sensing Volume 6, Issue 1
Show Author Affiliations
Ronggui Ma, Chang'an Univ. (China)
Weixing Wang, Chang'an Univ. (China)
Sheng Liu, Chang'an Univ. (China)

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