DEMs for Immersive Geographic Virtual Environments

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

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 Generate Digital Surface Model

Identification of DSM cells as bare earth / object

Create provisional DEM

Identify ground points from provisional DEM

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

I erode( I )

open( I )

Cross Section View of Image Opening

A sample progression of SMRF

When windowSize = [0 1 2 5 10 15], slope = 15% and elevationThreshold = .5

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

Identification of DSM cells as bare earth / object 1) Create a copy of the DSM called lastSurface 2) 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

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