SLOWLY MOVING TARGET DETECTION USING T-SNE AND SUPPORT VECTOR MACHINE

被引：0

作者：

Fang, Dan ^{[1
]}

Su, Jia ^{[1
]}

Li, Tao ^{[1
]}

Fan, Yifei ^{[1
]}

Tao, Mingliang ^{[1
]}

Liang, Jiawang ^{[1
]}

Shi, Jiao ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Xian 710072, Peoples R China

来源：

2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) | 2022年

基金：

中国国家自然科学基金;

关键词：

sea clutter; target detection; fractal-Time Frequency features; t-SNE; SEA CLUTTER;

D O I：

10.1109/IGARSS46834.2022.9883502

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

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.

引用

页码：883 / 886

页数：4

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