Road Extraction from Lidar Data Using Support Vector Machine Classification

Abstract

This paper presents a method for road extraction from lidar data based on support vector machine (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%, 60.26% and 66.71% quality for three datasets. Finally, centerline of the detected roads is extracted using mathematical morphology. Road information plays an important role in many modern applications, including transportation, automatic navigation systems, traffic management, and crisis management, and enables existing geographic information system (GIS) databases to be updated more efficiently. In the past two decades, automatic road extraction has become an important topic in remote sensing, photogrammetry, and computer vision. In addition, recent advances in lidar systems and their enormous potential in automatic feature extraction motivate the development of automatic road extraction algorithms based on lidar data.