University of Twente Student Theses


A segment-based approach for digital terrain model derivation in airborne laser scanning data

He, Yuxiang (2010) A segment-based approach for digital terrain model derivation in airborne laser scanning data.

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Abstract:A method for automatic extraction of Digital Terrain Model (DTM) from huge laser scanning data is presented. With the characteristics of LiDAR system, raw point clouds represent both terrain and non-terrain surface. In order to generate DTM, several methods were developed to remove non-terrain points. Algorithms for removing non-terrain points are named as filtering. The filtering method can be categorized as point-based and segment-based algorithms. Point-based filtering can not deal with landscape with discontinuities. Segment-based filtering, on the other hand, generates segments by clustering points based on surface fitting and uses topological and geometric properties for classification. Segment-based algorithms, by global looking of surroundings, can give more reliable results. Traditionally, filtering algorithms are performed and tested in small site. For the huge amount of point clouds, applying segmentation or segment-based filtering can not accomplish in one go in physical computer memory. To extract DTM from huge amount of LiDAR data, 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. Keywords: Huge ALS datasets, Splitting, Overlap, Segmentation, combination algorithm, Classification
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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