A new approach to automatic road extraction from satellite images using boosted classifiers

Umut Çinar; Ersin Karaman; Ekin Gedik; Yasemin Yardımcı; Uğur Halıcı

doi:10.1117/12.974693

8 November 2012 A new approach to automatic road extraction from satellite images using boosted classifiers

Umut Çinar, Ersin Karaman, Ekin Gedik, Yasemin Yardımcı, Uğur Halıcı

Proceedings Volume 8537, Image and Signal Processing for Remote Sensing XVIII; 85370O (2012) https://doi.org/10.1117/12.974693
Event: SPIE Remote Sensing, 2012, Edinburgh, United Kingdom

Abstract

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.

Citation Download Citation

Umut Çinar, Ersin Karaman, Ekin Gedik, Yasemin Yardımcı, and Uğur Halıcı "A new approach to automatic road extraction from satellite images using boosted classifiers", Proc. SPIE 8537, Image and Signal Processing for Remote Sensing XVIII, 85370O (8 November 2012); https://doi.org/10.1117/12.974693

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