Hierarchical Outlier Detection for Point Cloud Data Using a Density Analysis Method

doi:10.11947/j.AGCS.2015.20130423

Abstract

Abstract:

Laser scanning and image matching are both effective ways to get dense point cloud data, however, outliers obtained from both ways are still inevitable. A novel hierarchical outlier detection method is proposed for the automatic outlier detection of point cloud from image matching and airborne laser scanning. There are two main steps in this method. Firstly, the hierarchical density estimation is used to remove single and small cluster outliers. Then a progressive TIN method is used to find non-outliers removed in the previous steps. The experimental results indicate the effectiveness of this method in dealing with the two types of points cloud data. And this method can also handle low quality point cloud data from image matching. The quantitative analysis shows that the outlier detection rate is higher than 97%.

Key words: outlier detection, points cloud data, hierarchical, LiDAR, image matching

CLC Number:

P237

ZHU Junfeng, HU Xiangyun, ZHANG Zuxun, XIONG Xiaodong. Hierarchical Outlier Detection for Point Cloud Data Using a Density Analysis Method[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(3): 282-291.

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