Extracting road network from high resolution remotely sensed imagery is much difficult than from the other resolution because high resolution imagery exhibits more complex spectral character.To distinguish them from other spa-tial objects

novel classification tools should be applied in which support vector machine（SVM）is outstanding in its fast training speed and strong capability in non-linear classification tasks.A road network extracting method combining Gaus-sian Markov random field texture model（GMRF）and SVM is proposed.This method can be divided into two main steps:firstly

GMRF is used to obtain the6texture features values of sample pixels

and SVM is trained and then used to clas-sify the whole image with these features into road patches vs.non road patches.After that

the patches are initially con-nected with some morphological operations

and their axes are extracted with thinning operation and then vectorized.Se-condly

a heuristic connecting strategy is used to connect and group the axes of the road patches into final road network.Experiments of road network extracting from IKONOS imagery validate our method.