An improved frequency domain feature with partial least-squares dimensionality reduction for classifying buried threats in forward-looking ground-penetrating radar data

2017 ◽  
Author(s):  
Joseph A. Camilo ◽  
Miles Crosskey ◽  
Kenneth Morton ◽  
Leslie M. Collins ◽  
Jordan M. Malof
Keyword(s):  
Dimensionality Reduction ◽  
Least Squares ◽  
Frequency Domain ◽  
Partial Least Squares ◽  
Ground Penetrating Radar ◽  
Radar Data ◽  
Ground Penetrating ◽  
Forward Looking

Bayesian frequency-domain blind deconvolution of ground-penetrating radar data

2011 ◽  
Vol 75 (4) ◽  
pp. 615-630 ◽  
Author(s):  
C. Schmelzbach ◽  
F. Scherbaum ◽  
J. Tronicke ◽  
P. Dietrich
Keyword(s):  
Frequency Domain ◽  
Ground Penetrating Radar ◽  
Blind Deconvolution ◽  
Radar Data ◽  
Ground Penetrating

Determination of log moisture content using ground penetrating radar (GPR). Part 1. Partial least squares (PLS) method

Holzforschung ◽  
2015 ◽  
Vol 69 (9) ◽  
pp. 1117-1123 ◽  
Author(s):  
Guillaume Hans ◽  
David Redman ◽  
Brigitte Leblon ◽  
Joseph Nader ◽  
Armand La Rocque
Keyword(s):  
Moisture Content ◽  
Least Squares ◽  
Partial Least Squares ◽  
Ground Penetrating Radar ◽  
Black Spruce ◽  
Early Time ◽  
Quaking Aspen ◽  
Pls Regression ◽  
Reflected Waves ◽  
Ground Penetrating

Abstract Ground penetrating radar (GPR) is a handheld system showing good potential for the real-time and nondestructive characterization of wood moisture content (MC). However, measurements performed over logs can be challenging because of their curved surface that can affect the GPR signal. In this study, the MC of thawed and frozen logs was estimated for three species (quaking aspen, balsam poplar, and black spruce) using the full GPR signals and the partial least squares (PLS) regression method. The signal was acquired from the cross-section (CS) and through the bark (TB) of the logs with and without an aluminum plate placed under the log. The full GPR signal does not provide better log MC prediction accuracy for small logs compared with the early-time GPR signal. The information about the shape and diameter of the log is contained in the direct and reflected waves of the GPR signal. CS models provided more accurate log MC prediction (RMSEv=7–25%) than TB models (RMSEv=6–40%) for the hardwood species. Thawed and frozen log models showed similar performances. This study demonstrates that GPR in combination with PLS regression is suitable for predicting log MC in the field.

Change detection for detecting potential threats in forward-looking video and downward-looking ground-penetrating radar data

2011 ◽  
Author(s):  
Peter Torrione ◽  
Kenneth Morton, Jr. ◽  
Christopher Ratto ◽  
Michael Gunter ◽  
Leslie Collins
Keyword(s):  
Change Detection ◽  
Ground Penetrating Radar ◽  
Radar Data ◽  
Ground Penetrating ◽  
Forward Looking

Analysis of Jaycor's forward-looking ground-penetrating radar data

2000 ◽  
Author(s):  
Erik M. Rosen ◽  
Elizabeth Ayers ◽  
Darrell Bonn ◽  
Kelly D. Sherbondy ◽  
Charles A. Amazeen
Keyword(s):  
Ground Penetrating Radar ◽  
Radar Data ◽  
Ground Penetrating ◽  
Forward Looking
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