Real-Time Hyperbola Recognition and Fitting in GPR Data
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-666
- Type
- D - Journal article
- DOI
-
10.1109/TGRS.2016.2592679
- Title of journal
- IEEE Transactions on Geoscience and Remote Sensing
- Article number
- -
- First page
- 51
- Volume
- 55
- Issue
- 1
- ISSN
- 0196-2892
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2016
- URL
-
https://dx.doi.org/10.1109/TGRS.2016.2592679
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
3
- Research group(s)
-
B - AI (Artificial Intelligence)
- Citation count
- 53
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Thousands of surveys using Ground Penetrating Radar (GPR) are conducted every year to detect buried utilities, since utility statutory records do not always provide accurate location of buried utilities. Utilities show up as characteristic hyperbolic shapes in GPR images -- automating the process of discovering and geolocating such detections has the potential to significantly assist in the manual production utility maps, or indeed in automated mapping as in our subsequent work, e.g. https://doi.org/10.3390/s16111827 and https://doi.org/10.1016/j.autcon.2020.103229. More than 4000 full-text-views on the IEEE site. Informed work in €10m NetTUN Tunnel-Boring-Machine project(https://doi.org/10.1016/j.autcon.2018.03.002).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
