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The Potential of Virtual Reality Technology for Analysis of Remotely Sensed Data: A Lidar Case Study
Authors:Timothy Warner  M. Duane Nellis  Tomas Brandtberg  James B. McGraw  Joseph V. Gardner
Affiliation:1. Department of Geography and Geology , West Virginia University , Morgantown, WV, 26506-6300 E-mail: Tim.Warner@mail.wvu.edu Duane.Nellis@mail.wvu.edu Tomas.Brandtberg@telia.com;2. Department of Biology , West Virginia University , Morgantown, WV, 26506-6057 E-mail: jmcgraw@wvu.edu;3. Institute for Scientific Research Inc. , Fairmont, WV, 26555-2720 E-mail: jgardner@isr.us
Abstract:Abstract

Although the GIS community has been quick to exploit the advantages of virtual reality (VR) for display and analysis of spatial data, VR does not appear to have been exploited widely for remote sensing data analysis. A case study of high resolution lidar data acquired over a deciduous forest near Morgantown, WV was used to investigate the potential and limitations of current VR software for remote sensing analysis. The functionality within a standard remote sensing software package was found to provide a good overview of interpolated, smoothed lidar data, but was less useful for gridded data that had not been interpolated. With gridded data, it was possible to drape orthophotographs or other images over the lidar data, providing a useful method for investigating relationships between lidar and other data. Alternatively, using a commercial VR package, it was possible to view the original lidar point data, and thus visualize the multiple returns from within the canopy of each tree. The point data were preferable for identification of surfaces within the data cloud, especially the ground surface. For a fully integrated remote sensing VR package, functionality will be needed to link point and interpolated coverages, and also to enhance the interactive selection of data for further statistical analysis.
Keywords:land cover classification  pixel-based image analysis  object-based image analysis  hierarchical network classification  coal mining area
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