Simon Clode

Using the Dempster–Shafer method for the fusion of LIDAR data and multi-spectral images for building detection

Abstract A method for the detection of buildings in densely built-up urban areas by the fusion of first and last pulse laser scanner data and multi-spectral images is presented. The method attempts to achieve a classification of land cover into the classes “building”, “tree”, “grassland”, and “bare soil”, the latter three being considered relevant for the subsequent generation of a high-quality digital terrain model (DTM). Building detection is accomplished by first applying a hierarchical rule-based technique for coarse DTM generation based on morphological filtering. After that, data fusion based on the theory of Dempster–Shafer is used at two different stages of the classification process. We describe the algorithms involved, giving examples for a test site in Fairfield (New South Wales).

Detection and vectorization of roads from lidar data

A method for the automatic detection and vectorization of roads from lidar data is presented. To extract roads from a lidar point cloud, a hierarchical classification technique is used to classify the lidar points progressively into road and non-road points. During the classification process, both intensity and height values are initially used. Due to the homogeneous and consistent nature of roads, a local point density is introduced to finalize the classification. The resultant binary classification is then vectorized by convolving a complex-valued disk named the Phase Coded Disk (PCD) with the image to provide three separate pieces of information about the road. The centerline and width of the road are obtained from the resultant magnitude image while the direction is determined from the corresponding phase image, thus completing the vectorized road model. All algorithms used are described and applied to two urban test sites. Completeness values of 0.88 and 0.79 and correctness values of 0.67 and 0.80 were achieved for the classification phase of the process. The vectorization of the classified results yielded RMS values of 1.56 m and 1.66 m, completeness values of 0.84 and 0.81 and correctness values of 0.75 and 0.80 for two different data sets.

Data acquisition for 3D city models from LIDAR extracting buildings and roads

This paper deals with the automatic extraction of buildings and roads for 3D city models using LIDAR. The results presented in this paper show both the potential and the limitations of LIDAR data with respect to these tasks.