Articles | Volume 14, issue 1
https://doi.org/10.5194/amt-14-391-2021
https://doi.org/10.5194/amt-14-391-2021
Research article
 | 
18 Jan 2021
Research article |  | 18 Jan 2021

Classification of lidar measurements using supervised and unsupervised machine learning methods

Ghazal Farhani, Robert J. Sica, and Mark Joseph Daley

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Cited articles

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Doucet, P. J.: First aerosol measurements with the Purple Crow Lidar: lofted particulate matter straddling the stratospheric boundary, Master's thesis, The University of Western Ontario, London, ON, Canada, 2009. a, b
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Short summary
While it is relatively straightforward to automate the processing of lidar signals, it is difficult to automatically preprocess the measurements to distinguish between good and bad scans. It is easy to train humans to perform the task; however, considering the growing number of measurements, it is a time-consuming, on-going process. We have tested some machine learning algorithms for lidar signal classification and had success with both supervised and unsupervised methods.
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