This document proposes an adaptive morphological filtering approach for LiDAR point cloud data to generate digital elevation models (DEMs). Previous filtering methods require manually selecting parameters that impact results. The proposed approach uses an iterative process to adaptively adjust residual thresholds based on point discontinuities between iterations. It aims to minimize ground point removal errors and non-ground point classification errors. Initial tests on varied terrain show promising results, though discontinuous terrain still poses challenges for removing all type I errors of misclassifying ground points. Further refinement of stopping criteria is needed.

1. ADAPTIVE MORPHOLOGICAL FILTERING FOR DEM GENERATION KyoHyouk Kim and Jie Shan Geomatics Engineering School of Civil Engineering Purdue University July 27th 2011 IGARSS 2011 Annual Conference

2. OUTLINE Introduction Previous Works Proposed Approach Tests Summary 2/28

3. OUTLINE Introduction LiDAR Filtering Previous Works Proposed Approach Tests Summary 3/28

4. INTRODUCTION LiDAR (Light Detect And Ranging) One of remote sensing technologies providing point clouds with highly accurate collections of (X,Y,Z) at observed points. Vertical accuracy : 6 to 30 cm Horizontal accuracy : 10 to 46 cm Used as one of primary data sources to applications: DEM (Digital Elevation Model) Forest mapping Building footprints & 3D building reconstruction 4/28

5. INTRODUCTION Filtering Separation between ground points and nonground points. Ground points : Digital Elevation Model (DEM). Non-ground points : Building models, forest structure, canopy mapping In nearly all applications, filtering should be done first. Various filtering algorithms have been proposed. Slope-based filters Linear prediction filters Clustering (or segmentation) filters Morphological filters 5/28

6. INTRODUCTION Example 6/28

7. OUTLINE Introduction Previous Works General rule Morphological filtering algorithm Known issues & weaknesses Proposed Approach Tests Summary 7/28

8. PREVIOUS WORKS General rule What is ground surface ? Ground surfaces are relatively smooth & continuous Locally spanned through the lowest parts. EX: discontinuous ground surface and negative blunders. All filtering algorithms try to Identify points on the continuous and smooth ground surface Remove points lying on non-ground objects Measures of discontinuity Abrupt slope or elevation difference 8/28

9. PREVIOUS WORKS Morphological filtering Commonly used in the image processing fields to : Remove noise Enhance images Extract features Published papers for LiDAR filtering Kilian, Halla, and Englich (1996), Kilian et al. (1996), Zhang et al. (2003), Zaksek and Pfeifer (2006), Zhang and Cui (2007), Chen et al. (2007,2009). 9/28

10. PREVIOUS WORKS Principles Morphological opening operation : Erosion followed by dilation Erosion : Dilation : Objects with different size Apply different window size If terrain is not flat use maximum slope d0  d 10/28

11. PREVIOUS WORKS Known issues & weaknesses Parameters have a great impact on the filtering result Slope : allowed minimum elevation difference Window size : objects with various sizes Small window : Can not remove larger buildings Large window : Highly possible to flatten ground surface No optimal set of parameters supporting various types of terrain Require a priori information about the target area. Require trial-and-error process to find the best set of parameters 11/28

12. OUTLINE Introduction Previous Works Proposed Approach Adaptive morphological filtering Tests Summary 12/28

13. PROPOSED APPROACH Adaptive morphological filtering Use of regular grid with grid size g ( average point spacing) Cell with multiple points : point with the lowest elevation Empty cell : elevation of the nearest point Keeps the indices of original LiDAR points 1D (or 2D) Morphological erosion operation (W = 3). Filtering is applied only to points of discontinuity iteratively. Relevant parameters are adaptively adjusted in each iteration 13/28

14. PROPOSED APPROACH Use of 2D regular grid 14/28

15. PROPOSED APPROACH Discontinuity Measure Identify points lying on the edge between ground and objects Use of nominal value for thresholds (No fixed thresholds) Ex: 50, 75 or 90 percentile Threshold is determined before each iteration Residual Height difference Slope 15/28

16. PROPOSED APPROACH Example One LiDAR profile Slope Residual 16/28

17. PROPOSED APPROACH Workflow For i=1 to # of rows (or # of columns) Determine residuals (ri) of all points Ifri> N percentile of r (N=50,75,90) If hi - (New hi) > Hmin hi = New hi Until # of filtered points = 0 End 17/28

18. PROPOSED APPROACH Example 70th row - 50 percentile - Hmin = 1.0m Purdue campus 18/28

19. PROPOSED APPROACH Refinement Type I (omission) error : remove ground points mistakenly Caused by (1) negative blunders and (2) discontinuous ground Type II (commission) error : classify objects to ground points Caused by (1) nonground points with smaller elevation than Hmin 19/28

20. PROPOSED APPROACH Example ( Type I error) 20/28

21. PROPOSED APPROACH Resolve Type I error Identify continuous segments of ground points Distance of any non-ground points to the line (Si(n) – Si+1(1)) Repeated until no more ground points are restored d 21/28

22. OUTLINE Introduction Previous Works Proposed Approach Tests Summary of data sets Evaluations Summary 22/28

24. Small step-wise features

25. Discontinuous surface

26. Almost flat

27. Buildings mixed with trees

28. High relief (mixed slope)

29. Dense vegetation23/28 23/26

31. Residual threshold = 50 percentile24/28

33. Residual threshold = 50 percentile25/28

35. Residual threshold = 50 percentile26/28

36. OUTLINE Introduction Previous Works Proposed Approach Tests Summary 27/28

37. SUMMARY Adaptive morphological filtering Minimize the effects of parameters Filtering is applied only to points on the boundary iteratively Residual is used as the measure id discontinuity Residual threshold is adaptively adjusted with nominal value Promising results from various terrain conditions, but : Type I error is often significant in case of discontinuous terrain Need more robust stopping criterion 28/28