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Extracting roads based on Retinex and improved Canny operator with shape criteria in vague and unevenly illuminated aerial images
2012 (English)In: Journal of Applied Remote Sensing, ISSN 1931-3195, Vol. 6, 063610- p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2012. Vol. 6, 063610- p.
Keyword [en]
aerial image, road extraction, Retinex, edge detection, shape feature
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-119758DOI: 10.1117/1.JRS.6.063610ISI: 000315237200001ScopusID: 2-s2.0-84905455068OAI: diva2:612360

QC 20130321

Available from: 2013-03-21 Created: 2013-03-21 Last updated: 2013-03-21Bibliographically approved

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