Support vector machines for road extraction from remotely sensed images

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. © Springer-Verlag Berlin Heidelberg 2003.