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21 September 2015 Pattern recognition model for aerosol classification with atmospheric backscatter lidars: principles and simulations
Dong Liu, Yongying Yang, Yupeng Zhang, Zhongtao Cheng, Zhifei Wang, Jing Luo, Lin Su, Liming Yang, Yibing Shen, Jian Bai, Kaiwei Wang
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Journal of Applied Remote Sensing, Vol. 9, Issue 1, 096006 (September 2015). https://doi.org/10.1117/1.JRS.9.096006
Abstract
A pattern recognition model for aerosol classification with atmospheric backscatter lidars is proposed and studied in detail. The theoretical framework and the implementation process of the proposed model are presented. Computer simulations have been carried out to verify the practicability and robustness of this model. The k-fold cross-validation method is employed in the process of classifier designing to choose the proper decision rule, which is mainly based on statistical pattern recognition theory. At the same time, the validity of the model is evaluated. The generalized self-validation is also carried out in the computer simulations to verify the stability of the model. The analysis of the performances in reduced status, especially the instance of application to Cloud-Aerosol Lidar with Orthogonal Polarization, demonstrates the generalization ability and performance of this model.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Dong Liu, Yongying Yang, Yupeng Zhang, Zhongtao Cheng, Zhifei Wang, Jing Luo, Lin Su, Liming Yang, Yibing Shen, Jian Bai, and Kaiwei Wang "Pattern recognition model for aerosol classification with atmospheric backscatter lidars: principles and simulations," Journal of Applied Remote Sensing 9(1), 096006 (21 September 2015). https://doi.org/10.1117/1.JRS.9.096006
Published: 21 September 2015
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Cited by 12 scholarly publications.
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KEYWORDS
Aerosols
LIDAR
Atmospheric modeling
Atmospheric particles
Backscatter
Pattern recognition
Databases
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