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Classification of High-Altitude Flying Objects Based on Radiation Characteristics with Attention-Convolutional Neural Network and Gated Recurrent Unit Network

Authors :
Deen Dai
Lihua Cao
Yangfan Liu
Yao Wang
Zhaolong Wu
Source :
Remote Sensing, Vol 15, Iss 20, p 4985 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

In the task of classifying high-altitude flying objects, due to the limitations of the target flight altitude, there are issues such as insufficient contour information, low contrast, and fewer pixels in the target objects obtained through infrared detection technology, making it challenging to accurately classify them. In order to improve the classification performance and achieve the effective classification of the targets, this study proposes a high-altitude flying object classification algorithm based on radiation characteristic data. The target images are obtained through an infrared camera, and the radiation characteristics of the targets are measured using radiation characteristic measurement techniques. The classification is performed using an attention-based convolutional neural network (CNN) and gated recurrent unit (GRU) (referred to as ACGRU). In ACGRU, CNN-GRU and GRU-CNN networks are used to extract vectorized radiation characteristic data. The raw data are processed using Highway Network, and SoftMax is used for high-altitude flying object classification. The classification accuracy of ACGRU reaches 94.8%, and the F1 score reaches 93.9%. To verify the generalization performance of the model, comparative experiments and significance analysis were conducted with other algorithms on radiation characteristic datasets and 17 multidimensional time series datasets from UEA. The results show that the proposed ACGRU algorithm performs excellently in the task of high-altitude flying object classification based on radiation characteristics.

Details

Language :
English
ISSN :
20724292
Volume :
15
Issue :
20
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.6a91dd331f5f43d3b9dc14af88fee8c6
Document Type :
article
Full Text :
https://doi.org/10.3390/rs15204985