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A Lightning Classification Method Based on Convolutional Encoding Features

Authors :
Shunxing Zhu
Yang Zhang
Yanfeng Fan
Xiubin Sun
Dong Zheng
Yijun Zhang
Weitao Lyu
Huiyi Zhang
Jingxuan Wang
Source :
Remote Sensing, Vol 16, Iss 6, p 965 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

At present, for business lightning positioning systems, the classification of lightning discharge types is mostly based on lightning pulse signal features, and there is still a lot of room for improvement. We propose a lightning discharge classification method based on convolutional encoding features. This method utilizes convolutional neural networks to extract encoding features, and uses random forests to classify the extracted encoding features, achieving high accuracy discrimination for various lightning discharge events. Compared with traditional multi-parameter-based methods, the new method proposed in this paper has the ability to identify multiple lightning discharge events and does not require precise detailed feature engineering to extract individual pulse parameters. The accuracy of this method for identifying lightning discharge types in intra-cloud flash (IC), cloud-to-ground flash (CG), and narrow bipolar events (NBEs) is 97%, which is higher than that of multi-parameter methods. Moreover, our method can complete the classification task of lightning signals at a faster speed. Under the same conditions, the new method only requires 28.2 µs to identify one pulse, while deep learning-based methods require 300 µs. This method has faster recognition speed and higher accuracy in identifying multiple discharge types, which can better meet the needs of real-time business positioning.

Details

Language :
English
ISSN :
20724292
Volume :
16
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.3913515e71043e3b27e69ca7ebd1ed9
Document Type :
article
Full Text :
https://doi.org/10.3390/rs16060965