Multi-scale region growing point cloud filtering method based on surface fitting

doi:10.11947/j.AGCS.2020.20190142

Abstract

Abstract: Aiming at the problem of over-erosion and type II error accumulation by progressive triangle irregular network(TIN) densification(PTD), a multi-scale filtering method based on region growing is proposed. This method introduces pyramid strategy to establish different levels of point cloud, in which the low-level seed points are processed based on the high-level seed points. In the filtering process, the non-ground points are filtered by PTD first, and then the eroded ground seed points are compensated by the surface-fitting region growing algorithm with dynamic threshold determinated by local terrain, ultimately the real ground surface is gradually approached in loop iteration. By testing the 15 benchmark data sets provided by the ISPRS, Type I error, Type II error, Total error and Cohen’s kappa coefficient are 2.40%, 3.67%, 2.84% and 93.74% respectively, which shows that the proposed method has better performance to obtain the ideal ground model.

Key words: point cloud filtering, digital elevation model, triangle irregular network, data pyramid, region growing

CLC Number:

P237

ZHAN Zongqian, HU Mengqi, MAN Yiyun. Multi-scale region growing point cloud filtering method based on surface fitting[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(6): 757-766.

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