@inproceedings{124132c9430f4894bc79d23cdacf9efc,
title = "The Classification of Scattering Center Based on PointNet++",
abstract = "The category of scattering centers is one of the key parameters of attributed scattering center models. Currently, the classification of scattering centers mainly relies on methods such as Matrix Pencil and Shooting and Bouncing Ray. This paper creates a scattering center classification dataset based on surface current distributions and employs the PointNet++ deep learning architecture to learn and automatically annotate the geometric structure of strong scattering centers.",
keywords = "Feature extraction, Forward modeling, Scattering center model, Semantic segmentation",
author = "Li, \{Bo Tian\} and Jiuxiang Liu and Kunyi Guo and Jingyuan Han and Xinqing Sheng",
note = "Publisher Copyright: {\textcopyright} 2025 Applied Computational Electromagnetics Society.; 2025 International Applied Computational Electromagnetics Society Symposium, ACES-China 2025 ; Conference date: 08-08-2025 Through 11-08-2025",
year = "2025",
doi = "10.23919/ACES-China66523.2025.11332850",
language = "English",
series = "2025 International Applied Computational Electromagnetics Society Symposium, ACES-China 2025 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 International Applied Computational Electromagnetics Society Symposium, ACES-China 2025 - Proceedings",
address = "United States",
}