A COMPRESSED SENSING METHOD WITH ANALYTICAL RESULTS FOR LIDAR FEATURE CLASSIFICATION

Conference · Sat Jan 01 00:00:00 EST 2011

OSTI ID:1049061

Allen, Josef D ^[1]

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1. ORNL

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. Keywords: Compressed Sensing, Sparsity, Data Dictionary, LiDAR, ROC, K-Means, Clustering, K-SVD, Orthogonal Matching Pursuit

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: DE-AC05-00OR22725
• OSTI ID: 1049061
• Resource Relation: Conference: SPIE Defense Symposium, orlando, FL, USA, 20110425, 20110429
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ALGORITHMS
CLASSIFICATION
COMPUTERIZED SIMULATION
DETECTION
FLUID MECHANICS
LEAVES
METRICS
OPTICAL RADAR
PARTIAL DIFFERENTIAL EQUATIONS
PLANTS
PROBABILITY
SIMULATION
Compressed Sensing
Sparsity
Data Dictionary
LiDAR
ROC
K-Means
Clustering
K-SVD
Orthogonal Matching Pursuit