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    WE2.TO9.2.pptx WE2.TO9.2.pptx Presentation Transcript

    • 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
    • OUTLINE
      Introduction
      Previous Works
      Proposed Approach
      Tests
      Summary
      2/28
    • OUTLINE
      Introduction
      LiDAR
      Filtering
      Previous Works
      Proposed Approach
      Tests
      Summary
      3/28
    • 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
    • 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
    • INTRODUCTION
      Example
      6/28
    • OUTLINE
      Introduction
      Previous Works
      General rule
      Morphological filtering algorithm
      Known issues & weaknesses
      Proposed Approach
      Tests
      Summary
      7/28
    • 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
    • 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
    • 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
    • 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
    • OUTLINE
      Introduction
      Previous Works
      Proposed Approach
      Adaptive morphological filtering
      Tests
      Summary
      12/28
    • 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
    • PROPOSED APPROACH
      Use of 2D regular grid
      14/28
    • 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
    • PROPOSED APPROACH
      Example
      One LiDAR profile
      Slope
      Residual
      16/28
    • 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
    • PROPOSED APPROACH
      Example
      70th row
      - 50 percentile
      - Hmin = 1.0m
      Purdue campus
      18/28
    • 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
    • PROPOSED APPROACH
      Example ( Type I error)
      20/28
    • 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
    • OUTLINE
      Introduction
      Previous Works
      Proposed Approach
      Tests
      Summary of data sets
      Evaluations
      Summary
      22/28
    • 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
      23/26
    • INITIAL RESULTS
      Filtered ground surface (1)
      • Hmin= 1.5m
      • Residual threshold = 50 percentile
      24/28
    • INITIAL RESULTS
      Filtered ground surface (2)
      • Hmin = 1.0 m
      • Residual threshold = 50 percentile
      25/28
    • INITIAL RESULTS
      Filtered ground surface (3)
      • Hmin = 1.5 m
      • Residual threshold = 50 percentile
      26/28
    • OUTLINE
      Introduction
      Previous Works
      Proposed Approach
      Tests
      Summary
      27/28
    • 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