Abstract:
This paper proposes a new mapping schema, named Θ mapping, for filtering nonground objects from LiDAR data, and the generation of a digital terrain model. By extending th...Show MoreMetadata
Abstract:
This paper proposes a new mapping schema, named Θ mapping, for filtering nonground objects from LiDAR data, and the generation of a digital terrain model. By extending the CSL model, Θ mapping extracts the most contrasted connected-components from top-hat scale-space and attributes them for an adaptive multicriterion filter definition. Areas of the most contrasted connected-components and the standard deviations of contained points' levels are considered for this purpose. Computational efficiency is achieved by arranging the input LiDAR data into a grid, represented by a Max-Tree. Since a constant number of passes over the grid is required, the time complexity of the proposed method is linear according to the number of grid-cells. As confirmed by the experiments, the average CPU execution time decreases by nearly 98%, while the average accuracy improves by up to 10% in comparison with the related method.
Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 7, Issue: 1, January 2014)