@inproceedings{8762d1e9b6ba483399275e0b28557a1e,
title = "Analysis of Deep Learning 3-D Imaging Methods Based on UAV SAR",
abstract = "As an important development of traditional SAR 2-D imaging, Synthetic aperture radar (SAR) 3-D imaging's core is sparse signal processing. However, due to the nonlinear characteristics of sparse signal processing, it often needs iterative calculation, which makes it inefficient. Researchers have put forward some ideas of using deep learning neural networks to quickly solve nonlinear signal processing problems, but it is lack of comparative analysis of different network performances. Therefore, this paper analyzes the abilities of two deep learning neural networks (ISTA-Net and ADMM-Net) to solve the 3-D imaging problem of tomographic SAR. Their quantitative performance in imaging accuracy and imaging efficiency is emphatically discussed, which can provide theoretical reference for subsequent deep learning SAR 3-D imaging research. The effectiveness of the analysis is verified by the measured data of UAV SAR.",
keywords = "SAR 3-D imaging, accuracy, deep learning, efficiency",
author = "Changhao Liu and Yan Wang and Zegang Ding and Yangkai Wei and Jinyang Huang and Yawen Cai",
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/IGARSS46834.2022.9883292",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2951--2954",
booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",
address = "United States",
}
