@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",
}