Slowly Moving Target Detection Using t-SNE and Support Vector Machine

Dan Fang; Jia Su; Tao Li; Yifei Fan; Mingliang Tao; Jiawang Liang; Jiao Shi

doi:10.1109/IGARSS46834.2022.9883502

Slowly Moving Target Detection Using t-SNE and Support Vector Machine

Dan Fang, Jia Su, Tao Li, Yifei Fan, Mingliang Tao, Jiawang Liang, Jiao Shi

School of Electronics and Information

Northwestern Polytechnical University Xian

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

1 Scopus citations

Abstract

In this paper, a method using fractional signatures for small target detection is proposed based on fusion of features extracted from both the time-frequency domain and fractional domain by using principal component analysis (PCA) to get the key characteristics for redundancy reduction. The process of reducing feature dimensions is visualized by the t-distributed stochastic neighbor embedding (t-SNE) network, also the simulation based on real dataset offers better performance in small target detection under sea clutter environment.

Original language	English
Title of host publication	IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	883-886
Number of pages	4
ISBN (Electronic)	9781665427920
DOIs	https://doi.org/10.1109/IGARSS46834.2022.9883502
State	Published - 2022
Event	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia Duration: 17 Jul 2022 → 22 Jul 2022

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2022-July

Conference

Conference	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/Territory	Malaysia
City	Kuala Lumpur
Period	17/07/22 → 22/07/22

Keywords

fractal-Time Frequency features
sea clutter
t-SNE
target detection

Access to Document

10.1109/IGARSS46834.2022.9883502

Cite this

Fang, D., Su, J., Li, T., Fan, Y., Tao, M., Liang, J., & Shi, J. (2022). Slowly Moving Target Detection Using t-SNE and Support Vector Machine. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 883-886). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS46834.2022.9883502

@inproceedings{d23e6bfca6094a0cbac82a50d4c8d3d2,

title = "Slowly Moving Target Detection Using t-SNE and Support Vector Machine",

abstract = "In this paper, a method using fractional signatures for small target detection is proposed based on fusion of features extracted from both the time-frequency domain and fractional domain by using principal component analysis (PCA) to get the key characteristics for redundancy reduction. The process of reducing feature dimensions is visualized by the t-distributed stochastic neighbor embedding (t-SNE) network, also the simulation based on real dataset offers better performance in small target detection under sea clutter environment.",

keywords = "fractal-Time Frequency features, sea clutter, t-SNE, target detection",

author = "Dan Fang and Jia Su and Tao Li and Yifei Fan and Mingliang Tao and Jiawang Liang and Jiao Shi",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",

year = "2022",

doi = "10.1109/IGARSS46834.2022.9883502",

language = "英语",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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

pages = "883--886",

booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",

}

Fang, D, Su, J, Li, T, Fan, Y , Tao, M, Liang, J & Shi, J 2022, Slowly Moving Target Detection Using t-SNE and Support Vector Machine. in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2022-July, Institute of Electrical and Electronics Engineers Inc., pp. 883-886, 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022, Kuala Lumpur, Malaysia, 17/07/22. https://doi.org/10.1109/IGARSS46834.2022.9883502

Slowly Moving Target Detection Using t-SNE and Support Vector Machine. / Fang, Dan; Su, Jia; Li, Tao et al.
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc., 2022. p. 883-886 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July).

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

TY - GEN

T1 - Slowly Moving Target Detection Using t-SNE and Support Vector Machine

AU - Fang, Dan

AU - Su, Jia

AU - Li, Tao

AU - Fan, Yifei

AU - Tao, Mingliang

AU - Liang, Jiawang

AU - Shi, Jiao

PY - 2022

Y1 - 2022

N2 - In this paper, a method using fractional signatures for small target detection is proposed based on fusion of features extracted from both the time-frequency domain and fractional domain by using principal component analysis (PCA) to get the key characteristics for redundancy reduction. The process of reducing feature dimensions is visualized by the t-distributed stochastic neighbor embedding (t-SNE) network, also the simulation based on real dataset offers better performance in small target detection under sea clutter environment.

AB - In this paper, a method using fractional signatures for small target detection is proposed based on fusion of features extracted from both the time-frequency domain and fractional domain by using principal component analysis (PCA) to get the key characteristics for redundancy reduction. The process of reducing feature dimensions is visualized by the t-distributed stochastic neighbor embedding (t-SNE) network, also the simulation based on real dataset offers better performance in small target detection under sea clutter environment.

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KW - sea clutter

KW - t-SNE

KW - target detection

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U2 - 10.1109/IGARSS46834.2022.9883502

DO - 10.1109/IGARSS46834.2022.9883502

M3 - 会议稿件

AN - SCOPUS:85140370257

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 883

EP - 886

BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022

Y2 - 17 July 2022 through 22 July 2022

ER -

Fang D, Su J, Li T, Fan Y , Tao M, Liang J et al. Slowly Moving Target Detection Using t-SNE and Support Vector Machine. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc. 2022. p. 883-886. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS46834.2022.9883502

Slowly Moving Target Detection Using t-SNE and Support Vector Machine

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