Research Papers

Extracting roads based on Retinex and improved Canny operator with shape criteria in vague and unevenly illuminated aerial images

[+] Author Affiliations
Ma Ronggui

Chang’an University, School of Information Engineering, Xi’an, China

Wang Weixing

Chang’an University, School of Information Engineering, Xi’an, China

Royal Institute of Technology, Stockholm, Sweden

Liu Sheng

Chang’an University, School of Information Engineering, Xi’an, China

J. Appl. Remote Sens. 6(1), 063610 (Dec 03, 2012). doi:10.1117/1.JRS.6.063610
History: Received August 20, 2012; Revised October 24, 2012; Accepted November 6, 2012
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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.

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© 2012 Society of Photo-Optical Instrumentation Engineers

Citation

Ma Ronggui ; Wang Weixing and Liu Sheng
"Extracting roads based on Retinex and improved Canny operator with shape criteria in vague and unevenly illuminated aerial images", J. Appl. Remote Sens. 6(1), 063610 (Dec 03, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.063610


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