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State-of-the-Art: DTM Generation Using Airborne LIDAR Data

文献类型： 外文期刊

作者： Chen, Ziyue 1 ; Gao, Bingbo 2 ; Devereux, Bernard 3 ;

作者机构： 1.Beijing Normal Univ, Coll Global Change & Earth Syst Sci, 19 Xinjiekouwai St, Beijing 100875, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Univ Cambridge UK, Dept Geog, Cambridge CB2 3EN, England

关键词： DTM generation;surface-based;morphology-based;TIN-based;segmentation and classification;statistical analysis;multi-scale comparison

期刊名称：SENSORS （ 影响因子：3.576； 五年影响因子：3.735 ）

ISSN： 1424-8220

年卷期： 2017 年 17 卷 1 期

页码：

收录情况： SCI

摘要： 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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