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Road Centerline Extraction From High-Resolution Imagery Based on Shape Features and Multivariate Adaptive Regression Splines

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4 Author(s)
Zelang Miao ; Sch. of Environ. Sci. & Spatial Inf., China Univ. of Min. & Technol., Xuzhou, China ; Wenzhong Shi ; Hua Zhang ; Xinxin Wang

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.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:10 ,  Issue: 3 )

Date of Publication:

May 2013

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