Extraction of Road Network from High Resolution Remote Sensed Imagery with the Combination of Gaussian Markov Random Field Texture Model and Support Vector Machine
结合高斯马尔可夫随机场纹理模型与支撑向量机在高分辨率遥感图像上提取道路网

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.