ADAPTIVE MORPHOLOGICAL FILTERING FOR DEM GENERATION

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

23. INITIAL RESULTS Test data sets • High relief (mixed slope) • Small step-wise features • Discontinuous surface • Almost flat • Buildings mixed with trees • High relief (mixed slope) • Dense vegetation 23/28

24. INITIAL RESULTS Filtered ground surface (1) • Hmin= 1.5m • Residual threshold = 50 percentile 24/28

25. INITIAL RESULTS Filtered ground surface (2) • Hmin = 1.0 m • Residual threshold = 50 percentile 25/28

26. INITIAL RESULTS Filtered ground surface (3) • Hmin = 1.5 m • Residual threshold = 50 percentile 26/28

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

28. 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