科研之友
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摘要
LiDAR is widely used to collect point data-set representing the scanning scenes. Several products can be extracted from raw LiDAR data. DTM is among the most important. For traditional DTM detection approaches such as TIN-based and parametric methods, a negative outlier detector is employed beforehand since negative outliers will drive these methods to converge into an erroneous terrain surface. However, automatic outlier removal remains challenging in related fields. To overcome the obstacle, this article proposes minimum spanning tree-based detection method. An undirected graph is constructed for LiDAR points on basis of delauney rule. Edges are weighted by absolute slope value. Then, greedy algorithm is taken to generate MSF for the undirected graph. Edges with steep slopes are omitted in this process, which is how more than one MST arises. The DTM is generally the MST with largest area. Cases validate the effectiveness.
