Abstract
Support Vector Machines (SVMs) have received considerable attention from the pattern recognition community in recent years. They have been successfully applied to many classic recognition problems with results comparable or even superior to traditional classifiers such as decision trees, neural networks, maximum likelihood classifiers, etc. This paper presents encouraging experimental results from applying SVMs to the problem of road recognition and extraction from remotely sensed images using edge-based features.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chen, A., Donovan, G., Sowmya, A., Trinder, J.: Inductive Clustering: Automating Low-level Segmentation in High Resolution Images. In: Proc. ISPRS Commission III Symp. Photogrammetric Computer Vision., vol. 34, pp. 73–78 (2002)
Singh, S., Sowmya, A.: RAIL: Road Recognition from Aerial Images Using Inductive Learning. In: Proceedings of ISPRS Commission III Symposium Object Recognition and Scene Classification from multispectral and multisensor pixels, pp. 367–378
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Burges, C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Fukuda, S., Hirosawa, H.: Support Vector Machine Classification of Land Cover: Application to Polarimetric SAR Data. In: 2001 Asia-Pacific Radio Science Conference, Tokyo, Japan, pp. F5-04 (2001)
Huang, C., Davis, L., Townsend, J.: An Assessment of Support Vector Machines for Land Cover Classification. International Journal of Remote Sensing 4, 725–749 (2002)
Li, Z., Weida, Z., Licheng, J.: SAR Image Recognition Based on Support Vector Machines. In: Proceedings of the 2001 CIR International Conference on Radar, pp. 1044–1046 (2001)
Perkins, S., Harvey, N., Brumby, S., Lacker, K.: Support Vector Machines for Broad Area Feature Classification in Remotely Sensed Images. In: Proc. SPIE, vol. 4381 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yager, N., Sowmya, A. (2003). Support Vector Machines for Road Extraction from Remotely Sensed Images. In: Petkov, N., Westenberg, M.A. (eds) Computer Analysis of Images and Patterns. CAIP 2003. Lecture Notes in Computer Science, vol 2756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45179-2_36
Download citation
DOI: https://doi.org/10.1007/978-3-540-45179-2_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40730-0
Online ISBN: 978-3-540-45179-2
eBook Packages: Springer Book Archive