Research on Feature Extraction and Classification Algorithms for Infrared Targets

Xuqiang Yang; Yanbin Li; Yan Zhang; Chunling Yang; Su Ying Li

doi:10.1109/ICIEA54703.2022.10006197

Research on Feature Extraction and Classification Algorithms for Infrared Targets

Xuqiang Yang^*
, Yanbin Li
, Yan Zhang^*
, Chunling Yang^*
, Su Ying Li

^*Corresponding author for this work

School of Electrical Engineering and Automation

Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Infrared target detection techniques have been widely used in infrared alarming and reconnaissance, and the detection task is often done by machine learning. Feature extraction and classification algorithm are two core components of the machine learning. The selection of features and algorithm have a determinative effect on the detection result. Traditional target classification techniques only focus on only limited combinations of features and classification algorithm, which may result in a poor detection result. Based on this, this paper selects the gray features, statistical features, frequency domain features, and graphic features of the infrared target, and compares the three classification algorithms of KNN, Bayes, and SVM to give the optimal combination of features and classification algorithms by experiment.

Original language	English
Title of host publication	ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications
Editors	Wenxiang Xie, Shibin Gao, Xiaoqiong He, Xing Zhu, Jingjing Huang, Weirong Chen, Lei Ma, Haiyan Shu, Wenping Cao, Lijun Jiang, Zeliang Shu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1612-1617
Number of pages	6
ISBN (Electronic)	9781665409841
DOIs	https://doi.org/10.1109/ICIEA54703.2022.10006197
State	Published - 2022
Event	17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022 - Chengdu, China Duration: 16 Dec 2022 → 19 Dec 2022

Publication series

Name	ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications

Conference

Conference	17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022
Country/Territory	China
City	Chengdu
Period	16/12/22 → 19/12/22

Keywords

Classification algorithm
Feature extraction
Infrared target detection

Access to Document

10.1109/ICIEA54703.2022.10006197

Cite this

Yang, X., Li, Y., Zhang, Y., Yang, C., & Li, S. Y. (2022). Research on Feature Extraction and Classification Algorithms for Infrared Targets. In W. Xie, S. Gao, X. He, X. Zhu, J. Huang, W. Chen, L. Ma, H. Shu, W. Cao, L. Jiang, & Z. Shu (Eds.), ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications (pp. 1612-1617). (ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIEA54703.2022.10006197

Yang, Xuqiang ; Li, Yanbin ; Zhang, Yan et al. / Research on Feature Extraction and Classification Algorithms for Infrared Targets. ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications. editor / Wenxiang Xie ; Shibin Gao ; Xiaoqiong He ; Xing Zhu ; Jingjing Huang ; Weirong Chen ; Lei Ma ; Haiyan Shu ; Wenping Cao ; Lijun Jiang ; Zeliang Shu. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 1612-1617 (ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications).

@inproceedings{f8821f37aacd400db26e2614a4ba4b08,

title = "Research on Feature Extraction and Classification Algorithms for Infrared Targets",

abstract = "Infrared target detection techniques have been widely used in infrared alarming and reconnaissance, and the detection task is often done by machine learning. Feature extraction and classification algorithm are two core components of the machine learning. The selection of features and algorithm have a determinative effect on the detection result. Traditional target classification techniques only focus on only limited combinations of features and classification algorithm, which may result in a poor detection result. Based on this, this paper selects the gray features, statistical features, frequency domain features, and graphic features of the infrared target, and compares the three classification algorithms of KNN, Bayes, and SVM to give the optimal combination of features and classification algorithms by experiment.",

keywords = "Classification algorithm, Feature extraction, Infrared target detection",

author = "Xuqiang Yang and Yanbin Li and Yan Zhang and Chunling Yang and Li, \{Su Ying\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022 ; Conference date: 16-12-2022 Through 19-12-2022",

year = "2022",

doi = "10.1109/ICIEA54703.2022.10006197",

language = "英语",

series = "ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1612--1617",

editor = "Wenxiang Xie and Shibin Gao and Xiaoqiong He and Xing Zhu and Jingjing Huang and Weirong Chen and Lei Ma and Haiyan Shu and Wenping Cao and Lijun Jiang and Zeliang Shu",

booktitle = "ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications",

address = "美国",

}

Yang, X, Li, Y, Zhang, Y , Yang, C & Li, SY 2022, Research on Feature Extraction and Classification Algorithms for Infrared Targets. in W Xie, S Gao, X He, X Zhu, J Huang, W Chen, L Ma, H Shu, W Cao, L Jiang & Z Shu (eds), ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications. ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications, Institute of Electrical and Electronics Engineers Inc., pp. 1612-1617, 17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022, Chengdu, China, 16/12/22. https://doi.org/10.1109/ICIEA54703.2022.10006197

Research on Feature Extraction and Classification Algorithms for Infrared Targets. / Yang, Xuqiang; Li, Yanbin; Zhang, Yan et al.
ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications. ed. / Wenxiang Xie; Shibin Gao; Xiaoqiong He; Xing Zhu; Jingjing Huang; Weirong Chen; Lei Ma; Haiyan Shu; Wenping Cao; Lijun Jiang; Zeliang Shu. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1612-1617 (ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Research on Feature Extraction and Classification Algorithms for Infrared Targets

AU - Yang, Xuqiang

AU - Li, Yanbin

AU - Zhang, Yan

AU - Yang, Chunling

AU - Li, Su Ying

PY - 2022

Y1 - 2022

N2 - Infrared target detection techniques have been widely used in infrared alarming and reconnaissance, and the detection task is often done by machine learning. Feature extraction and classification algorithm are two core components of the machine learning. The selection of features and algorithm have a determinative effect on the detection result. Traditional target classification techniques only focus on only limited combinations of features and classification algorithm, which may result in a poor detection result. Based on this, this paper selects the gray features, statistical features, frequency domain features, and graphic features of the infrared target, and compares the three classification algorithms of KNN, Bayes, and SVM to give the optimal combination of features and classification algorithms by experiment.

AB - Infrared target detection techniques have been widely used in infrared alarming and reconnaissance, and the detection task is often done by machine learning. Feature extraction and classification algorithm are two core components of the machine learning. The selection of features and algorithm have a determinative effect on the detection result. Traditional target classification techniques only focus on only limited combinations of features and classification algorithm, which may result in a poor detection result. Based on this, this paper selects the gray features, statistical features, frequency domain features, and graphic features of the infrared target, and compares the three classification algorithms of KNN, Bayes, and SVM to give the optimal combination of features and classification algorithms by experiment.

KW - Classification algorithm

KW - Feature extraction

KW - Infrared target detection

UR - https://www.scopus.com/pages/publications/85146867648

U2 - 10.1109/ICIEA54703.2022.10006197

DO - 10.1109/ICIEA54703.2022.10006197

M3 - 会议稿件

AN - SCOPUS:85146867648

T3 - ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications

SP - 1612

EP - 1617

BT - ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications

A2 - Xie, Wenxiang

A2 - Gao, Shibin

A2 - He, Xiaoqiong

A2 - Zhu, Xing

A2 - Huang, Jingjing

A2 - Chen, Weirong

A2 - Ma, Lei

A2 - Shu, Haiyan

A2 - Cao, Wenping

A2 - Jiang, Lijun

A2 - Shu, Zeliang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022

Y2 - 16 December 2022 through 19 December 2022

ER -

Yang X, Li Y, Zhang Y , Yang C, Li SY. Research on Feature Extraction and Classification Algorithms for Infrared Targets. In Xie W, Gao S, He X, Zhu X, Huang J, Chen W, Ma L, Shu H, Cao W, Jiang L, Shu Z, editors, ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1612-1617. (ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications). doi: 10.1109/ICIEA54703.2022.10006197

Research on Feature Extraction and Classification Algorithms for Infrared Targets

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Keywords

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