Improving the detection of wind fields from LIDAR aerosol backscatter using feature extraction

Brady R. Bickel, Eric R. Rotthoff, Gage S. Walters, Timothy Joseph Kane, Shane D. Mayor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The tracking of winds and atmospheric features has many applications, from predicting and analyzing weather patterns in the upper and lower atmosphere to monitoring air movement from pig and chicken farms. Doppler LIDAR systems exist to quantify the underlying wind speeds, but cost of these systems can sometimes be relatively high, and processing limitations exist. The alternative is using an incoherent LIDAR system to analyze aerosol backscatter. Improving the detection and analysis of wind information from aerosol backscatter LIDAR systems will allow for the adoption of these relatively low cost instruments in environments where the size, complexity, and cost of other options are prohibitive. Using data from a simple aerosol backscatter LIDAR system, we attempt to extend the processing capabilities by calculating wind vectors through image correlation techniques to improve the detection of wind features.

Original languageEnglish (US)
Title of host publicationOptical Pattern Recognition XXVII
EditorsDavid Casasent, Mohammad S. Alam
PublisherSPIE
Volume9845
ISBN (Electronic)9781510600867
DOIs
StatePublished - Jan 1 2016
EventOptical Pattern Recognition XXVII - Baltimore, United States
Duration: Apr 20 2016Apr 21 2016

Other

OtherOptical Pattern Recognition XXVII
CountryUnited States
CityBaltimore
Period4/20/164/21/16

Fingerprint

Aerosol
Aerosols
pattern recognition
Feature Extraction
Feature extraction
aerosols
costs
Costs
chickens
lower atmosphere
swine
Wind Speed
upper atmosphere
Processing
Doppler
weather
Weather
Farms
Atmosphere
Quantify

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Bickel, B. R., Rotthoff, E. R., Walters, G. S., Kane, T. J., & Mayor, S. D. (2016). Improving the detection of wind fields from LIDAR aerosol backscatter using feature extraction. In D. Casasent, & M. S. Alam (Eds.), Optical Pattern Recognition XXVII (Vol. 9845). [98450A] SPIE. https://doi.org/10.1117/12.2223932
Bickel, Brady R. ; Rotthoff, Eric R. ; Walters, Gage S. ; Kane, Timothy Joseph ; Mayor, Shane D. / Improving the detection of wind fields from LIDAR aerosol backscatter using feature extraction. Optical Pattern Recognition XXVII. editor / David Casasent ; Mohammad S. Alam. Vol. 9845 SPIE, 2016.
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Bickel, BR, Rotthoff, ER, Walters, GS, Kane, TJ & Mayor, SD 2016, Improving the detection of wind fields from LIDAR aerosol backscatter using feature extraction. in D Casasent & MS Alam (eds), Optical Pattern Recognition XXVII. vol. 9845, 98450A, SPIE, Optical Pattern Recognition XXVII, Baltimore, United States, 4/20/16. https://doi.org/10.1117/12.2223932

Improving the detection of wind fields from LIDAR aerosol backscatter using feature extraction. / Bickel, Brady R.; Rotthoff, Eric R.; Walters, Gage S.; Kane, Timothy Joseph; Mayor, Shane D.

Optical Pattern Recognition XXVII. ed. / David Casasent; Mohammad S. Alam. Vol. 9845 SPIE, 2016. 98450A.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Bickel BR, Rotthoff ER, Walters GS, Kane TJ, Mayor SD. Improving the detection of wind fields from LIDAR aerosol backscatter using feature extraction. In Casasent D, Alam MS, editors, Optical Pattern Recognition XXVII. Vol. 9845. SPIE. 2016. 98450A https://doi.org/10.1117/12.2223932