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Thomas J. Pingel & Keith C. Clarke Department of Geography University of California, Santa Barbara

DEMs for Immersive Geographic Virtual Environments: An Improved Simple Morphological Filter for Terrain Classification of LIDAR Data . Thomas J. Pingel & Keith C. Clarke Department of Geography University of California, Santa Barbara. AAG Annual Meeting, New York City, 24 Feb 2012.

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Thomas J. Pingel & Keith C. Clarke Department of Geography University of California, Santa Barbara

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  1. DEMs for Immersive Geographic Virtual Environments: An Improved Simple Morphological Filter for Terrain Classification of LIDAR Data  Thomas J. Pingel & Keith C. Clarke Department of Geography University of California, Santa Barbara AAG Annual Meeting, New York City, 24 Feb 2012

  2. Project Overview Build real-time geodatabasesfrom audio and video feeds, and project them onto an immersive virtual world. This immersive visualization is intended to aid in the understanding of a very recent or in-progress local event.

  3. The test bed: Isla Vista & the campus at UC Santa Barbara

  4. Good terrain layers are fundamental. • Any errors will propagate through the rest of the VE construction process. • Misshapen ground layers are confusing to the eye. • A good ground layer can replace some kinds of extra information likely to be lacking.

  5. Requirements • A good LIDAR-to-DEM production tool should be • Efficient with computation and memory • Validated against samples • Flexible • Urban, suburban, and rural environments • Highly differentiated terrain • Integrated • Specialized software is hard to validate • It lengthens the production chain, making automation difficult. • A tool oriented to produce DEMs for visualization (instead of analysis) has particular issues as well.

  6. General Workflow Diagram Generate Digital Surface Model Identification of DSM cells as bare earth / object Create provisional DEM Identify ground points from provisional DEM

  7. Morphological Opening open( I ) = dilate(erode( I ) ) I erode( I ) open( I )

  8. Cross Section View of Image Opening

  9. A sample progression of SMRF When windowSize = [0 1 2 5 10 15], slope = 15% and elevationThreshold = .5

  10. Other Notable Filters • Zhang et al. (2003) • Exponentially increasing window size • Slope threshold based on difference in window sizes between steps • Chen et al. (2007) • Applied a different method for vegetation and buildings • Object “prospects” were evaluated based on the distribution of slopes around the perimeter • Other notable algorithms (not PMFs) • Axelsson (1999) - Adaptive TIN • Shao (2007) – Climbing and Sliding • Meng et al. (2009) – Multidirectional

  11. Measuring Performance • ISPRS Datasets • Sithole & Vosselman (2003 & 2004) • 15 samples in urban and rural environments • Less dense than most modern systems (.67 & .18 RPSM) • Type I Error • BE as Object • causes “holes” in the DEM→ overly smooth areas • Type II Error • Object as BE • causes overly rough areas • Total Error & Cohen’s Kappa

  12. [DTM groundIDs] = smrf(x,y,z,c,wk,s,[e1 e2]) • c – cell size • Related to resolution of input data • wk – maximum window size • Vector of increasing values up to the size of the largest feature to be removed. • s – slope threshold • Value of largest common terrain slope • Establishes elevation threshold for each step • e – elevation threshold • Difference from digital terrain model (DTM) that is still identified as ground. • Slope dependent threshold

  13. Identification of DSM cells as bare earth / object • Create a copy of the DSM called lastSurface • For thisWindow = 1 to maxWindow • thisThreshold = slope * (thisWindow / cellSize) • thisSurface = open(lastSurface,disk(thisWindow)) • groundMask = groundMask OR (lastSurface – thisSurface > thisThreshold) • lastSurface = thisSurface

  14. SMRF vs. other PMFs • Oriented to reducing Type I error, while maintaining acceptable Type II error rates • Built to be as simple as possible to provide a solid base from which to test novel techniques • Linearly increasing window size, one-parameter based slope thresholding • Uses PDE-based image inpainting instead of nearest neighbor / kriging • Accepts a slope-based thresholding parameter for provisional DEM to ground ID stage • Optional “net-cutting” routine to remove large buildings on differentiated terrain.

  15. How well does SMRF perform? • Single Parameter • Mean Total Error = 4.4% • Axelsson (4.82), Chen (7.23), Shao (4.20) • Mean Kappa = 85.4% • Axelsson (84.19), Meng (79.93) • Optimized • Mean Total Error = 2.97% • Mean Kappa = 90.02%

  16. Future Work • Public testing: search for LIDAR + SMRF online • Investigate more complex subroutines for performance benefits • Data structures for VR display • Level of Detail, Grids / TINs • Immersive DEM correction • Building reconstruction • True orthovideo overlay

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