Automatic road extraction from LIDAR data based on classifier fusion

The ultimate goal of pattern recognition systems in remote sensing is to achieve the best possible classification performance for recognition of different objects such as buildings, roads and trees. From a scientific perspective, the extraction of roads in complex environments is one of the challenging issues in photogrammetry and computer vision, since many tasks related to automatic scene interpretation are involved. Roads have homogeneous reflectivity in LIDAR intensity and the same height as bare surface in elevation. Proposed method in this paper is based on combining multiple classifiers (MCS) is one of the most important topics in pattern recognition to achieve higher accuracy. Majority Voting and Selective Naïve Bays are two methods that used for fusion of classifiers.