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

• Published in: Journal of applied remote sensing Vol. 6; no. 1; p. 063610
• Main Authors: Ronggui, Ma, Weixing, Wang, Sheng, Liu
• Format: Journal Article
• Language: English
• Published: Society of Photo-Optical Instrumentation Engineers 2012
• Subjects: aerial image; edge detection; Retinex; road extraction; shape feature; aerial image; road extraction; edge detection; Retinex; shape feature
• ISSN: 1931-3195; 1931-3195
• DOI: 10.1117/1.JRS.6.063610

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.