With the characteristics of LIDAR system, raw point clouds represent both terrain and non-terrain surface. In order to generate DTM, the paper introduces one improved filtering method based on the segment-based algorithms. The method generates segments by clustering points based on surface fitting and uses topological and geometric properties for classification. In the process, three major steps are involved. First, the whole datasets is split into several small overlapping tiles. For each tile, by removing wall and vegetation points, accurate segments are found. The segments from all tiles are assigned unique segment number. In the following step, topological descriptions for the segment distribution pattern and height jump between adjacent segments are identified in each tile. Based on the topology and geometry, segment-based filtering algorithm is performed for classification in each tile. Then, based on the spatial location of the segment in one tile, two confidence levels are assigned to the classified segments. The segments with low confidence level are because of losing geometric or topological information in one tile. Thus, a combination algorithm is generated to detect corresponding parts of incomplete segment from multiple tiles. Then another classification algorithm is performed for these segments. The result of these segments will have high confidence level. After that, all the segments in one tile have high confidence level of classification result. The final DTM will add all the terrain segments and avoid duplicate points. At the last of the paper, the experiment show the filtering result and be compared with the other classical filtering methods, the analysis proves the method has advantage in the precision of DTM. But because of the complicated algorithms, the processing speed is little slower, that is the future improvement which should been researched.