A Computationally Efficient Airborne Forward-Looking Super-Resolution Imaging Method Based on Sparse Bayesian Learning-Reference-Cited by-同舟云学术

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A Computationally Efficient Airborne Forward-Looking Super-Resolution Imaging Method Based on Sparse Bayesian Learning

Published:2023 Issue: Volume:61 Page:1-13
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Li Weixin¹^ORCID,Li Ming¹^ORCID,Zuo Lei¹^ORCID,Chen Hongmeng²^ORCID,Wu Yan³^ORCID,Zhuo Zhenyu¹

Affiliation:

1. National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China

2. Beijing Institute of Radio Measurement, Beijing, China

3. Remote Sensing Image Processing and Fusion Group, School of Electronic Engineering, Xidian University, Xi’an, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10077591.pdf?arnumber=10077591

Reference37 articles.

1. Resolution improvement of ERS scatterometer data over land by Wiener filtering;l;Remote Sens Environ,2000

2. Bayesian Forward-Looking Superresolution Imaging Using Doppler Deconvolution in Expanded Beam Space for High-Speed Platform

3. Real Aperture Radar Forward-Looking Imaging Based on Variational Bayesian in Presence of Outliers

4. Airborne Forward-Looking Radar Super-Resolution Imaging Using Iterative Adaptive Approach

5. Balanced Tikhonov and Total Variation Deconvolution Approach for Radar Forward-Looking Super-Resolution Imaging

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