Voxel-based attribute profiles on lidar data for land cover mapping
Résumé
This paper deals with strategies for LiDAR data analysis. While a large majority of studies first rasterize 3D point clouds onto regular 2D grids and then use 2D image processing tools for characterizing data, our work rather suggests to keep as long as possible the 3D structure by computing features on 3D data and rasterize later in the process. By this way, the vertical component is still taken into account. In practice, a voxelization step of raw data is performed in order to exploit mathematical tools defined on regular volumes. More precisely, we focus on attribute profiles that have been shown to be very efficient features to characterize remote sensing scenes. They require the computation of an underlying hierarchical structure (through a Max-Tree). Experimental results obtained on urban LiDAR data classification support the performances of this strategy compared with an early rasterization process.
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