A feature learning approach for classifying buried threats in forward looking ground penetrating radar data

2016 ◽  
Author(s):  
Joseph A. Camilo ◽  
Jordan M. Malof ◽  
Leslie M. Collins
Keyword(s):  
Ground Penetrating Radar ◽  
Feature Learning ◽  
Radar Data ◽  
Learning Approach ◽  
Ground Penetrating ◽  
Forward Looking

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

Reconstructing Properties of Subsurface from Ground-Penetrating Radar Data

PIERS Online ◽  
2006 ◽  
Vol 2 (6) ◽  
pp. 567-572
Author(s):  
Hui Zhou ◽  
Dongling Qiu ◽  
Takashi Takenaka
Keyword(s):  
Ground Penetrating Radar ◽  
Radar Data ◽  
Ground Penetrating

scholarly journals Ice thickness distribution of all Swiss glaciers based on extended ground-penetrating radar data and glaciological modeling

2021 ◽  
pp. 1-19
Author(s):  
Melchior Grab ◽  
Enrico Mattea ◽  
Andreas Bauder ◽  
Matthias Huss ◽  
Lasse Rabenstein ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Thickness Distribution ◽  
Radar Data ◽  
Tourism Industry ◽  
Swiss Alps ◽  
Ice Thickness ◽  
Hazard Management ◽  
Bed Topography ◽  
Ice Volume ◽  
Ground Penetrating

Abstract Accurate knowledge of the ice thickness distribution and glacier bed topography is essential for predicting dynamic glacier changes and the future developments of downstream hydrology, which are impacting the energy sector, tourism industry and natural hazard management. Using AIR-ETH, a new helicopter-borne ground-penetrating radar (GPR) platform, we measured the ice thickness of all large and most medium-sized glaciers in the Swiss Alps during the years 2016–20. Most of these had either never or only partially been surveyed before. With this new dataset, 251 glaciers – making up 81% of the glacierized area – are now covered by GPR surveys. For obtaining a comprehensive estimate of the overall glacier ice volume, ice thickness distribution and glacier bed topography, we combined this large amount of data with two independent modeling algorithms. This resulted in new maps of the glacier bed topography with unprecedented accuracy. The total glacier volume in the Swiss Alps was determined to be 58.7 ± 2.5 km3 in the year 2016. By projecting these results based on mass-balance data, we estimated a total ice volume of 52.9 ± 2.7 km3 for the year 2020. Data and modeling results are accessible in the form of the SwissGlacierThickness-R2020 data package.

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