@inproceedings{fd3d3794ff2a474eba4aa1ba8a4d33a3,
title = "Insect Species Identification Based on Polarimetric Scattering Feature Sequences Using MsRNN",
abstract = "This study introduces an original deep learning technique for the classification of insect species, which relies on the analysis of polarimetric scattering feature sequences. Utilizing a multi-scale Recurrent Neural Network (MsRNN) architecture, we achieve high-precision recognition of nine first-class migratory pest species using data collected from a high-resolution fully polarimetric radar. Our method achieves an average identification accuracy of 98.1%, demonstrating significant improvements compared to traditional classification techniques. This high accuracy makes our approach a valuable tool for pest management and agricultural protection, enabling more efficient and accurate monitoring of harmful migratory insect species.",
keywords = "insect radar, polarimetric scattering, RNN, species identification",
author = "Fan Zhang and Weidong Li and Jiahao Ren and Chunxu Gong and Rui Wang and Yifan Li",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024 ; Conference date: 22-11-2024 Through 24-11-2024",
year = "2024",
doi = "10.1109/ICSIDP62679.2024.10868268",
language = "English",
series = "IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024",
address = "United States",
}