@inproceedings{a38c4eba1cd14f6fac5b646597f9b0f4,
title = "An improved 3-D reconstruction method based on deep neural network",
abstract = "Current three-dimensional (3-D) reconstruction methods based on two-dimensional (2-D) inverse synthetic aperture (ISAR) image sequences usually consist of some sequential nonlinear steps, and they face with the error accumulation and transmission inevitably. To realize precise target reconstruction, an improved 3-D reconstruction method based on motion parameters and deep neural network (DNN) is proposed. The proposed method could realize the end-To-end transformation from the motion parameters to the 3-D target via DNN, and the error transmission and accumulation can be avoided. Results based on the synthetized data set validate the proposed method.",
keywords = "3-D reconstruction, Deep neural network, Inverse synthetic aperture radar",
author = "Tianyi Zhang and Zegang Ding and Siyuan Liu and Yangkai Wei and Guanxing Wang and Yan Zhang and Xin Guo and Yongpeng Gao",
note = "Publisher Copyright: {\textcopyright} 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.; 14th European Conference on Synthetic Aperture Radar, EUSAR 2022 ; Conference date: 25-07-2022 Through 27-07-2022",
year = "2022",
language = "English",
series = "Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "789--793",
booktitle = "EUSAR 2022 - 14th European Conference on Synthetic Aperture Radar",
address = "United States",
}