A New Approach to Automatic Road Extraction from Satellite Images using Boosted Classifiers

Date

2012-09-26

Author

CINAR, UMUT
KARAMAN, ERSİN
GEDİK, EKIN
ÇETİN, YASEMİN
Halıcı, Uğur

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this study, a supervised method for automatic road detection based on spectral indices and structural properties is proposed. The need of generalizing the spectral features for the images captured by different kinds of devices is investigated. Mean-shift segmentation algorithm is employed to partition the input multi-spectral image in addition to k-means which is used as a complementary method for structural feature generation. Adaboost learning algorithm is utilized with extracted features to distinguish roads from non-road regions in the satellite images. The proposed algorithm is tested on an image database containing both IKONOS and GEOEYE images to verify the achieved generalization. The empirical results show that the proposed road extraction method is promising and capable of finding the majority of the road network.

Subject Keywords

Road extraction, Remote sensing, Adaptive boosting, Spectral band ratios, Structural features

URI

https://hdl.handle.net/11511/38222

DOI

https://doi.org/10.1117/12.974693

Conference Name

Conference on Image and Signal Processing for Remote Sensing XVIII

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

U. CINAR, E. KARAMAN, E. GEDİK, Y. ÇETİN, and U. Halıcı, “A New Approach to Automatic Road Extraction from Satellite Images using Boosted Classifiers,” Edinburgh, SCOTLAND, 2012, vol. 8537, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38222.