Automatic Road Extraction From Remote Sensing Images Based on a Normalized Second Derivative Map

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In this letter, we propose a novel automatic algorithm for road extraction from remote sensing images. The algorithm includes low- and high-level processing. In the low-level processing, we determine a normalized second derivative map of road profiles of a generalized bar shape, which is width invariant and contrast proportional, and accordingly obtain initial road center pixels. In the high-level processing, using the map and initial center pixels, we initially determine road segments. The segments are then locally refined using their orientation randomness and length-to-width ratio and further refined via global graph-cut optimization. A final road network is thereby extracted in a robust manner. Experimental results demonstrate that the proposed algorithm provides noticeably more robust and higher road extraction performance in various images compared with the existing algorithms.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2015-09
Language
English
Article Type
Article
Citation

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, v.12, no.9, pp.1858 - 1862

ISSN
1545-598X
DOI
10.1109/LGRS.2015.2431268
URI
http://hdl.handle.net/10203/208508
Appears in Collection
EE-Journal Papers(저널논문)
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