Airborne laser scanning (ALS) is one of the LIDAR technologies (Light Detection and Ranging). It provides information about the terrain in form of a point cloud. During measurement is acquired: spatial data (object’s coordinates X, Y, Z) and collateral data such as intensity of reflected signal. The obtained point cloud is typically applied for generating a digital terrain model (DTM) and a digital surface model (DSM). For DTM and DSM generation it is necessary to apply filtration or classification algorithms. They allow to divide a point cloud into object groups (e.g.: terrain points, vegetation, etc.). In this study classification is conducted with one extra parameter – intensity. The obtained point groups were used for digital spatial model generation. Classification is a time and work consuming process, therefore there is a need to reduce the time of ALS point cloud processing. Optimization algorithm enables to decrease the number of points in a dataset. In this study the main goal was to test the impact of optimization on the results of a classification. Studies were conducted in two variants. Variant 1 includes classification of the original point cloud where points are divided in the groups: roofs, asphalt road, tree/bushes, grass. On variant 2 before classification, an optimization algorithm was performed in the original point cloud. Obtained from these two variants object groups were used to generate a spatial model, which was then statistically analyzed.
Authors
Additional information
- Category
- Publikacja w czasopiśmie
- Type
- artykuły w czasopismach recenzowanych i innych wydawnictwach ciągłych
- Language
- angielski
- Publication year
- 2013
