Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data
Abstract
An image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinate system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.
- Authors:
-
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- National Center for Atmospheric Research, Boulder, CO (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- National Center for Atmospheric Research (NCAR), Boulder, CO (United States)
- OSTI Identifier:
- 1247117
- Report Number(s):
- NREL/JA-6A20-64224
Journal ID: ISSN 0739-0572
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Atmospheric and Oceanic Technology
- Additional Journal Information:
- Journal Volume: 33; Journal Issue: 4; Journal ID: ISSN 0739-0572
- Publisher:
- American Meteorological Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; observational techniques and algorithms; algorithms; data processing; lidars/lidar observations; mathematical and statistical techniques; classification
Citation Formats
Weekley, R. Andrew, Goodrich, R. Kent, and Cornman, Larry B. Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data. United States: N. p., 2016.
Web. doi:10.1175/JTECH-D-15-0125.1.
Weekley, R. Andrew, Goodrich, R. Kent, & Cornman, Larry B. Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data. United States. https://doi.org/10.1175/JTECH-D-15-0125.1
Weekley, R. Andrew, Goodrich, R. Kent, and Cornman, Larry B. Wed .
"Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data". United States. https://doi.org/10.1175/JTECH-D-15-0125.1. https://www.osti.gov/servlets/purl/1247117.
@article{osti_1247117,
title = {Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data},
author = {Weekley, R. Andrew and Goodrich, R. Kent and Cornman, Larry B.},
abstractNote = {An image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinate system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.},
doi = {10.1175/JTECH-D-15-0125.1},
journal = {Journal of Atmospheric and Oceanic Technology},
number = 4,
volume = 33,
place = {United States},
year = {Wed Apr 06 00:00:00 EDT 2016},
month = {Wed Apr 06 00:00:00 EDT 2016}
}
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Works referenced in this record:
Lidar detection algorithm for time and range anomalies
journal, January 2007
- Ben-David, Avishai; Davidson, Charles E.; Vanderbeek, Richard G.
- Applied Optics, Vol. 46, Issue 29
Automated gust front detection using knowledge-based signal processing
conference, January 1993
- Delanoy, R. L.; Troxel, S. W.
- The Record of the 1993 IEEE National Radar Conference
Wavelet-Based Optical Flow for Two-Component Wind Field Estimation from Single Aerosol Lidar Data
journal, October 2015
- Dérian, Pierre; Mauzey, Christopher F.; Mayor, Shane D.
- Journal of Atmospheric and Oceanic Technology, Vol. 32, Issue 10
Optimization of the Cross-Correlation Algorithm for Two-Component Wind Field Estimation from Single Aerosol Lidar Data and Comparison with Doppler Lidar
journal, January 2016
- Hamada, Masaki; Dérian, Pierre; Mauzey, Christopher F.
- Journal of Atmospheric and Oceanic Technology, Vol. 33, Issue 1
A comparison study of image segmentation by clustering techniques
conference, January 1996
- Kettaf, F. Z.; Bi, D.; Asselin de Beauville, J. P.
- Proceedings of Third International Conference on Signal Processing (ICSP'96)
Time-series novelty detection using one-class support vector machines
conference, January 2003
- Ma, J.; Perkins, S.
- 2003 International Joint Conference on Neural Networks, Proceedings of the International Joint Conference on Neural Networks, 2003.
Raman-shifted eye-safe aerosol lidar
journal, January 2004
- Mayor, Shane D.; Spuler, Scott M.
- Applied Optics, Vol. 43, Issue 19
Polarization lidar at
journal, September 2007
- Mayor, Shane D.
- Optical Engineering, Vol. 46, Issue 9
Comparison of aerosol backscatter and wind field estimates from the REAL and the SAMPLE
conference, September 2015
- Mayor, Shane D.; Dérian, Pierre; Mauzey, Christopher F.
- SPIE Optical Engineering + Applications, SPIE Proceedings
The NIMA Method for Improved Moment Estimation from Doppler Spectra
journal, March 2002
- Morse, Corinne S.; Goodrich, Robert K.; Cornman, Larry B.
- Journal of Atmospheric and Oceanic Technology, Vol. 19, Issue 3
An Algorithm for Classification and Outlier Detection of Time-Series Data
journal, January 2010
- Weekley, R. Andrew; Goodrich, Robert K.; Cornman, Larry B.
- Journal of Atmospheric and Oceanic Technology, Vol. 27, Issue 1
Works referencing / citing this record:
Preliminary Studies on Atmospheric Monitoring by Employing a Portable Unmanned Mie-Scattering Scheimpflug Lidar System
journal, April 2019
- Liu, Zhi; Li, Limei; Li, Hui
- Remote Sensing, Vol. 11, Issue 7