State-of-the-Art: DTM Generation Using Airborne LIDAR Data
AbstractDigital terrain model (DTM) generation is the fundamental application of airborne Lidar data. In past decades, a large body of studies has been conducted to present and experiment a variety of DTM generation methods. Although great progress has been made, DTM generation, especially DTM generation in specific terrain situations, remains challenging. This research introduces the general principles of DTM generation and reviews diverse mainstream DTM generation methods. In accordance with the filtering strategy, these methods are classified into six categories: surface-based adjustment; morphology-based filtering, triangulated irregular network (TIN)-based refinement, segmentation and classification, statistical analysis and multi-scale comparison. Typical methods for each category are briefly introduced and the merits and limitations of each category are discussed accordingly. Despite different categories of filtering strategies, these DTM generation methods present similar difficulties when implemented in sharply changing terrain, areas with dense non-ground features and complicated landscapes. This paper suggests that the fusion of multi-sources and integration of different methods can be effective ways for improving the performance of DTM generation. View Full-Text
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Chen, Z.; Gao, B.; Devereux, B. State-of-the-Art: DTM Generation Using Airborne LIDAR Data. Sensors 2017, 17, 150.
Chen Z, Gao B, Devereux B. State-of-the-Art: DTM Generation Using Airborne LIDAR Data. Sensors. 2017; 17(1):150.Chicago/Turabian Style
Chen, Ziyue; Gao, Bingbo; Devereux, Bernard. 2017. "State-of-the-Art: DTM Generation Using Airborne LIDAR Data." Sensors 17, no. 1: 150.
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