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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. - PowerPoint PPT Presentation
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DEMs for Immersive Geographic Virtual Environments: An Improved Simple Morphological Filter
for Terrain Classification of LIDAR Data
Thomas J. Pingel & Keith C. ClarkeDepartment of Geography
University of California, Santa Barbara
AAG Annual Meeting, New York City, 24 Feb 2012
Project Overview
Build real-time geodatabases from 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.
The test bed:Isla Vista & the campus at UC Santa Barbara
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.
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.
General Workflow Diagram
Identification of DSM cells as bare earth / object
Generate Digital Surface Model
Identify ground points from provisional DEM
Create provisional DEM
open( I ) = dilate(erode( I ) )
I
erode( I )
open( I )
Morphological Opening
Cross Section View of Image Opening
When windowSize = [0 1 2 5 10 15], slope = 15% and elevationThreshold = .5
A sample progression of SMRF
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
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
• [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
1) Create a copy of the DSM called lastSurface2) For thisWindow = 1 to maxWindow
a) thisThreshold = slope * (thisWindow / cellSize) b) thisSurface = open(lastSurface,disk(thisWindow))c) groundMask = groundMask OR
(lastSurface – thisSurface > thisThreshold)
d) lastSurface = thisSurface
Identification of DSM cells as bare earth / object
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.
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%
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