期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2009
卷号：XXXVIII-3/W8
页码：81-86
出版社：Copernicus Publications
摘要：LIDAR is a powerful remote sensing technology for acquisition of 3D information from terrain surface. Algorithms used for LIDAR data in urban area are used mostly to deal with the 3D points cloud and to identify objects, such as buildings, trees and roads. In contrast to the well studied problem of building and tree detection from LIDAR data, the detection of roads from LIDAR is in its formative years. Road detection from remotely sensed data is a difficult problem that requires more research due to the many unsolved questions related to scene interpretation. Existing road extraction techniques are characterised by poor detection rates and the need for existing data and / or user interaction. To improve the potential of road extraction process, other information in addition to the height of cloud points is required, such as laser intensity. However, few algorithms for classification using intensity data have been deeply investigated. The laser intensity differs from material to material. The intensity of reflection for most instances of the same material is similar; while pulse intensity from different materials differs. This article deals with using as much of the recorded laser information as possible thus both height and intensity are used. To extract roads from an LIDAR point cloud, a multiple classifier system is used to classify the LIDAR points into road and other non-road objects. We experiment classifier selection and combination in classification of LIDAR data over an urban area, wherein we aim to select an optimal or sub-optimal subset of classifiers from available combination candidates through an evolutionary strategy. The performance of the selected classifiers is measured by the combination accuracy using plurality of votes. The obtained results show that optimum subset classifier selection, improves the combination performance compared to the combination of all classifiers
