This study focuses on enhancing the visualisation of FW LiDAR data. The intensity profile of each full-waveform pulse is accumulated into a voxel array, building up a fully-3D representation of the returned intensities. The 3D representation is then polygonised using functional representation (FRep) of geometric objects. In addition to using the higher resolution FW data, the voxels can accumulate evidence from multiple pulses, which confers greater noise resistance. Moreover, this approach opens up possibilities of vertical observation of data, while the pulses are emitted in different angles. Multi-resolution rendering and visualisation of entire flightlines are also allowed. Introduction: The most common approach of interpreting the data, so far, was decomposition of the signal into a sum of Gaussian functions and sequentially extraction of points clouds from the waves (Wanger, Ullrich, Ducic, Malzer , & Studnicka, 2006). Neunschwander et al used this approach for Landover classification (Neuenschwander, Magruder, & Tyler, 2009) while Reightberger et al applied it for distinguishing deciduous trees from coniferous trees (Reitberger, Krzystek, & Stilla, 2006). In 2007, Chauve et al proposed an approach of improving the Gaussian model in order to increase the density of the points cloud extracted from the data and consequently improve point based classifications applied on full-waveform LiDAR data (Chauve, Mallet, Bretar, Durrieu, Deseilligny, & Puech, 2007).
|Title of host publication||RSPSoc Annual Conference|
|Publication status||Published - 2014|