Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 10, Issue 3, Pages 583-587Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2012.2214761
Keywords
High-resolution imagery; multivariate adaptive regression splines (MARS); road centerline extraction; shape feature
Categories
Funding
- Priority Academic Program Development of Jiangsu Higher Education Institutions [SZBF2011-6-B35]
- Ministry of Science and Technology of China [2012BAJ15B04, 2012AA12A305]
- Hong Kong Polytechnic University [1-ZV4F, G-U753, G-YF24, G-YJ75, G-YG66]
- Innovation Project for Graduate Students of Jiangsu Province [CX10B_143Z]
- National Natural Science Foundation of China [41201451]
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Road centerline extraction from remotely sensed imagery can be used to update a Geographic Information System (GIS) database. The common road extraction from high-resolution imagery is based on spectral information only; it is difficult to separate road features from background completely, and a thinning algorithm always results in short spurs which reduce the smoothness of the road centerline. To overcome the aforementioned shortcomings of the common existing road centerline algorithms, this letter presents a new method to extract the road centerline from high-resolution imagery based on shape features and multivariate adaptive regression splines (MARS), in which potential road segments were obtained based on shape features and spectral feature, followed by MARS to extract road centerlines. Two experiments are performed to evaluate the accuracy of the proposed method.
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