Unsupervised TomoSAR Image Reconstruction Through Virtual Multiple Measurement Explorations

Tomographic synthetic aperture radar (TomoSAR) reconstruction produces 3D imaging of scenes from measurements and has recently been combined with data-driven deep learning techniques. While most existing methods rely on supervised learning with simulation-based and paired training samples, we focus...

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Bibliographic Details

Published in:	Proceedings of the IEEE National Radar Conference (1996) pp. 321 - 326
Main Authors:	Liu, Liang, Zeng, Tianjiao, Wang, Mou, Shi, Jun, Wei, Shunjun, Zhang, Xiaoling, Zhan, Xu
Format:	Conference Proceeding
Language:	English
Published:	IEEE 04.10.2025
Subjects:	data-driven Deep learning Image reconstruction Loss measurement neural network inductive bias Noise Noise measurement Physics Redundancy Three-dimensional displays tomoSAR Training Unsupervised learning
ISSN:	2375-5318
Online Access:	Get full text
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