Support Vector Machines for Road Extraction from Remotely Sensed Images

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.

[1]  Arcot Sowmya,et al.  INDUCTIVE CLUSTERING: AUTOMATING LOW-LEVEL SEGMENTATION IN HIGH RESOLUTION IMAGES * , 2002 .

[2]  Simon J. Perkins,et al.  Support vector machines for broad-area feature classification in remotely sensed images , 2001, SPIE Defense + Commercial Sensing.

[3]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[4]  S. Fukuda,et al.  Support vector machine classification of land cover: application to polarimetric SAR data , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[5]  Zhang Li,et al.  SAR image recognition based on support vector machines , 2001, 2001 CIE International Conference on Radar Proceedings (Cat No.01TH8559).

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  Salesh Singh Arcot Sowmya,et al.  RAIL : Road Recognition from Aerial Images Using Inductive Learning , 1998 .