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Proceedings Paper

Automatic forest canopy removal algorithm for underneath obscure target detection by airborne lidar point cloud data
Author(s): Li-Der Chang; K. Clint Slatton; Vivek Anand; Pang-Wei Liu; Heezin Lee; Michael V. Campbell
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Paper Abstract

The thermal imaging cameras can see the heat signature of people, boats, and vehicles in total darkness as well as through smoke, haze, and light fog, but not through the forest canopy. This study develops a novel algorithm to help detecting obscure targets underneath forest canopy and mitigate the vegetation problem for those bare ground point extraction filters as well. By examining our automatically processed results with actual LiDAR data, the forest canopy is successfully removed where all obscure vehicles or buildings underneath canopy can now be easily seen. Besides, the occluded rate of forest canopy and the detailed underneath x-y point distribution can be easily obtained accordingly. This will be very useful for predicting the performance of occluded target detection with respect to various object locations.

Paper Details

Date Published: 29 April 2010
PDF: 12 pages
Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 766424 (29 April 2010); doi: 10.1117/12.849822
Show Author Affiliations
Li-Der Chang, Univ. of Florida (United States)
K. Clint Slatton, Univ. of Florida (United States)
Vivek Anand, Univ. of Florida (United States)
Pang-Wei Liu, Univ. of Florida (United States)
Heezin Lee, Univ. of Florida (United States)
Michael V. Campbell, U.S. Army Engineer Research and Development Ctr. (United States)

Published in SPIE Proceedings Vol. 7664:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV
Russell S. Harmon; John H. Holloway Jr.; J. Thomas Broach, Editor(s)

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