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Proceedings Paper

A compressed sensing method with analytical results for lidar feature classification
Author(s): Josef D. Allen; Jiangbo Yuan; Xiuwen Liu; Mark Rahmes
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Paper Abstract

We present an innovative way to autonomously classify LiDAR points into bare earth, building, vegetation, and other categories. One desirable product of LiDAR data is the automatic classification of the points in the scene. Our algorithm automatically classifies scene points using Compressed Sensing Methods via Orthogonal Matching Pursuit algorithms utilizing a generalized K-Means clustering algorithm to extract buildings and foliage from a Digital Surface Models (DSM). This technology reduces manual editing while being cost effective for large scale automated global scene modeling. Quantitative analyses are provided using Receiver Operating Characteristics (ROC) curves to show Probability of Detection and False Alarm of buildings vs. vegetation classification. Histograms are shown with sample size metrics. Our inpainting algorithms then fill the voids where buildings and vegetation were removed, utilizing Computational Fluid Dynamics (CFD) techniques and Partial Differential Equations (PDE) to create an accurate Digital Terrain Model (DTM) [6]. Inpainting preserves building height contour consistency and edge sharpness of identified inpainted regions. Qualitative results illustrate other benefits such as Terrain Inpainting's unique ability to minimize or eliminate undesirable terrain data artifacts.

Paper Details

Date Published: 26 April 2011
PDF: 12 pages
Proc. SPIE 8055, Optical Pattern Recognition XXII, 80550G (26 April 2011); doi: 10.1117/12.884370
Show Author Affiliations
Josef D. Allen, Oak Ridge National Lab. (United States)
Jiangbo Yuan, The Florida State Univ. (United States)
Xiuwen Liu, The Florida State Univ. (United States)
Mark Rahmes, Harris Corp. (United States)

Published in SPIE Proceedings Vol. 8055:
Optical Pattern Recognition XXII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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