The critical step in using Lidar data is the data pre-processing. Choosing the proper filtering method plays an important role in the quality of results. Actually, it cannot be expected that the quality of the result should be better than the data accuracy itself. On the other side, all interpolation methods have no difficulties to generate precise 3D models since dense enough Lidar data is available.

2.1.1 Filtering

The purpose of filtering is to remove the vegetation points. Figure 3 shows all points before
filtering (figure 3, left) and terrain points after filtering (figure 3, right).

The terrain points extracted from the point cloud of Lidar data set are used as an input to generate a DTM. The first pulse data sets contain vegetation points and terrain points in the forest area. Numerous kinds of filtering methods are developed to classify the terrain and vegetation points in the point cloud (Pfeifer et. al., 2004; Tovari and Pfeifer, 2005). Different concepts for filtering, with different complexity and performance characteristics have been proposed in mainly four categories such as morphological, progressive densification, surface based, segmentation based filter. There are also developments, extensions, and variants for these filter methods.

The morphological filter was derived by Vosselman (2000) from the mathematical morphology definition. It works in such a way that the smaller are the distances between a ground point and its neighboring points, the lesser is the height difference. Based on this criterion the method can properly eliminate the outliers. The progressive densification filter is developed by Axelsson (Axelsson, 2000). This filter works progressively by classifying points which belong to the ground. Surface based filters assume at the beginning that all points lying on the ground form a surface. Then a fitting procedure is applied to extract the points which do not belong to the ground. This method goes back to Pfeifer et al. (2001). Segmentation filters are developed as the fourth category. Segment is a group of points which are located within defined thresholds such as the distance and height difference between neighbor points. Sithole (2005) introduced a segment classification method by performing region growing techniques referring to Tovari and Pfeifer (2005). It works by classifying segments into as many classes as possible (Filin and Pfeifer, 2006).

The experimental comparison of filtering algorithms with manual methods for DTM extraction is introduced by Sithole and Vosselman (2004) to show the suitability of filters with the terrain shape. In comparison with other filtering methods, segment base filter is turned out to be a more reliable method in steep slope terrain extraction using a surface growing method (Sithole and Vosselman 2005).

Fig. 4. Segmentation method, point cloud from vertical view

The most important part in this method is the accuracy assessment and parameter tuning. These processes for the segmentation method are performed by Vazirabad and Karslioglu (2009) as shown in figure 4. Segmented terrain points are coloured as brown and green while white points are assumed to be the vegetation points in forest area.