Road Extraction from Lidar Data Using Support Vector Machine Classification

This paper presents a method for road extraction from lidar data based on SVM classification. The lidar data are used exclusively to evaluate the potential in the road extraction process. First, the SVM algorithm is used to classify the lidar data into five classes: road, tree, building, grassland, and cement. Then, some misclassified pixels in the road class is removed using the road values in the normalized Digital Surface Model and Normalized Difference Distance features. In the postprocessing stage, a method based on Radon transform and Spline interpolation is employed to automatically locate and fill the gaps in the road network. The experimental results show that the proposed algorithm for gap filling works well on straight roads. The proposed road extraction algorithm is tested on three datasets. An accuracy assessment indicated 63.7 percent, 60.26 percent and 66.71 percent quality for three datasets. Finally, centerline of the detected roads is extracted using mathematical morphology.