State-of-the-Art: DTM Generation Using Airborne LIDAR Data
1
College of Global Change and Earth System Science, Beijing Normal University, 19 Xinjiekouwai Street, Beijing 100875, China
2
Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
3
Department of Geography, University of Cambridge UK, CB2 3EN Cambridge, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Sensors 2017, 17(1), 150; https://doi.org/10.3390/s17010150
Received: 25 October 2016
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Revised: 8 December 2016
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Accepted: 24 December 2016
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Published: 14 January 2017
(This article belongs to the Section Remote Sensors)
Digital 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.
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Keywords:
DTM generation; surface-based; morphology-based; TIN-based; segmentation and classification; statistical analysis; multi-scale comparison
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MDPI and ACS Style
Chen, Z.; Gao, B.; Devereux, B. State-of-the-Art: DTM Generation Using Airborne LIDAR Data. Sensors 2017, 17, 150. https://doi.org/10.3390/s17010150
AMA Style
Chen Z, Gao B, Devereux B. State-of-the-Art: DTM Generation Using Airborne LIDAR Data. Sensors. 2017; 17(1):150. https://doi.org/10.3390/s17010150
Chicago/Turabian StyleChen, Ziyue, Bingbo Gao, and Bernard Devereux. 2017. "State-of-the-Art: DTM Generation Using Airborne LIDAR Data" Sensors 17, no. 1: 150. https://doi.org/10.3390/s17010150
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