20 May 2013 3D graph segmentation for target detection in FOPEN LiDAR data
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Abstract
A novel use of Felzenszwalb’s graph based efficient image segmentation algorithm* is proposed for segmenting 3D volumetric foliage penetrating (FOPEN) Light Detection and Ranging (LiDAR) data for automated target detection. The authors propose using an approximate nearest neighbors algorithm to establish neighbors of points in 3D and thus form the graph for segmentation. Following graph formation, the angular difference in the points’ estimated normal vectors is proposed for the graph edge weights. Then the LiDAR data is segmented, in 3D, and metrics are calculated from the segments to determine their geometrical characteristics and thus likelihood of being a target. Finally, the bare earth within the scene is automatically identified to avoid confusion of flat bare earth with flat targets. The segmentation, the calculated metrics, and the bare earth all culminate in a target detection system deployed for FOPEN LiDAR. General purpose graphics processing units (GPGPUs) are leveraged to reduce processing times for the approximate nearest neighbors and point normal estimation algorithms such that the application can be run in near real time. Results are presented on several data sets.
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Nicholas Shorter, Judson Locke, O'Neil Smith, Emma Keating, Philip Smith, "3D graph segmentation for target detection in FOPEN LiDAR data", Proc. SPIE 8731, Laser Radar Technology and Applications XVIII, 873108 (20 May 2013); doi: 10.1117/12.2016128; https://doi.org/10.1117/12.2016128
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