Automated multi-classifier recognition of atmospheric turbulent structures obtained by Doppler lidar

Automated multi-classifier recognition of atmospheric turbulent structures obtained by Doppler lidar

We present algorithms and results of automated processing of LiDAR measurements obtained during VEGILOT measuring campaign in Paris in autumn 2014 in order to study horizontal turbulent atmospheric regimes on urban scales. To process images obtained by horizontal atmospheric scanning using Doppler L...

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Main Authors: Sokolov Anton, Dmitriev Egor, Cheliotis Ioannis, Delbarre Hervé, Dieudonne Elsa, Augustin Patrick, Fourmentin Marc
Format: Article
Language:English
Published: EDP Sciences 2020-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/83/e3sconf_rpers20_03013.pdf
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spelling doaj-22ff8daada9a49f4864c5c61c573fed32021-04-02T16:40:28ZengEDP SciencesE3S Web of Conferences2267-12422020-01-012230301310.1051/e3sconf/202022303013e3sconf_rpers20_03013Automated multi-classifier recognition of atmospheric turbulent structures obtained by Doppler lidarSokolov Anton0Dmitriev Egor1Cheliotis Ioannis2Delbarre Hervé3Dieudonne Elsa4Augustin Patrick5Fourmentin Marc6University of Littoral Cote d’Opale, Laboratory for Physico-Chemistry of the AtmosphereInstitute of Numerical Mathematics of Russian Academy of SciencesUniversity of Littoral Cote d’Opale, Laboratory for Physico-Chemistry of the AtmosphereUniversity of Littoral Cote d’Opale, Laboratory for Physico-Chemistry of the AtmosphereUniversity of Littoral Cote d’Opale, Laboratory for Physico-Chemistry of the AtmosphereUniversity of Littoral Cote d’Opale, Laboratory for Physico-Chemistry of the AtmosphereUniversity of Littoral Cote d’Opale, Laboratory for Physico-Chemistry of the AtmosphereWe present algorithms and results of automated processing of LiDAR measurements obtained during VEGILOT measuring campaign in Paris in autumn 2014 in order to study horizontal turbulent atmospheric regimes on urban scales. To process images obtained by horizontal atmospheric scanning using Doppler LiDAR, the method is proposed based on texture analysis and classification using supervised machine learning algorithms. The results of the parallel classification by various classifiers were combined using the majority voting strategy. The obtained estimates of accuracy demonstrate the efficiency of the proposed method for solving the problem of remote sensing of regional-scale turbulent patterns in the atmosphere.https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/83/e3sconf_rpers20_03013.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Sokolov Anton
Dmitriev Egor
Cheliotis Ioannis
Delbarre Hervé
Dieudonne Elsa
Augustin Patrick
Fourmentin Marc
spellingShingle Sokolov Anton
Dmitriev Egor
Cheliotis Ioannis
Delbarre Hervé
Dieudonne Elsa
Augustin Patrick
Fourmentin Marc
Automated multi-classifier recognition of atmospheric turbulent structures obtained by Doppler lidar
E3S Web of Conferences
author_facet Sokolov Anton
Dmitriev Egor
Cheliotis Ioannis
Delbarre Hervé
Dieudonne Elsa
Augustin Patrick
Fourmentin Marc
author_sort Sokolov Anton
title Automated multi-classifier recognition of atmospheric turbulent structures obtained by Doppler lidar
title_short Automated multi-classifier recognition of atmospheric turbulent structures obtained by Doppler lidar
title_full Automated multi-classifier recognition of atmospheric turbulent structures obtained by Doppler lidar
title_fullStr Automated multi-classifier recognition of atmospheric turbulent structures obtained by Doppler lidar
title_full_unstemmed Automated multi-classifier recognition of atmospheric turbulent structures obtained by Doppler lidar
title_sort automated multi-classifier recognition of atmospheric turbulent structures obtained by doppler lidar
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2020-01-01
description We present algorithms and results of automated processing of LiDAR measurements obtained during VEGILOT measuring campaign in Paris in autumn 2014 in order to study horizontal turbulent atmospheric regimes on urban scales. To process images obtained by horizontal atmospheric scanning using Doppler LiDAR, the method is proposed based on texture analysis and classification using supervised machine learning algorithms. The results of the parallel classification by various classifiers were combined using the majority voting strategy. The obtained estimates of accuracy demonstrate the efficiency of the proposed method for solving the problem of remote sensing of regional-scale turbulent patterns in the atmosphere.
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/83/e3sconf_rpers20_03013.pdf
work_keys_str_mv AT sokolovanton automatedmulticlassifierrecognitionofatmosphericturbulentstructuresobtainedbydopplerlidar
AT dmitrievegor automatedmulticlassifierrecognitionofatmosphericturbulentstructuresobtainedbydopplerlidar
AT cheliotisioannis automatedmulticlassifierrecognitionofatmosphericturbulentstructuresobtainedbydopplerlidar
AT delbarreherve automatedmulticlassifierrecognitionofatmosphericturbulentstructuresobtainedbydopplerlidar
AT dieudonneelsa automatedmulticlassifierrecognitionofatmosphericturbulentstructuresobtainedbydopplerlidar
AT augustinpatrick automatedmulticlassifierrecognitionofatmosphericturbulentstructuresobtainedbydopplerlidar
AT fourmentinmarc automatedmulticlassifierrecognitionofatmosphericturbulentstructuresobtainedbydopplerlidar
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