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Communication Dans Un Congrès Année : 2020

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

Résumé

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.
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Origine : Publication financée par une institution
Licence : CC BY - Paternité

Dates et versions

hal-04294837 , version 1 (22-11-2023)

Identifiants

Citer

Anton Sokolov, Egor Dmitriev, Ioannis Cheliotis, Hervé Delbarre, Elsa Dieudonne, et al.. Automated multi-classifier recognition of atmospheric turbulent structures obtained by Doppler lidar. Regional Problems of Earth Remote Sensing, RPERS 2020, Sep 2020, Krasnoyarsk, Russia. pp.03013, ⟨10.1051/e3sconf/202022303013⟩. ⟨hal-04294837⟩
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