Fast SAR autofocus based on convolutional neural networks

被引：0
|
作者：
Liu Z. [1 ]
Yang S. [1 ]
Yu Z. [1 ]
Feng Z. [1 ]
Gao Q. [1 ]
Wang M. [2 ]
机构：
[1] Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an
[2] National Key Laboratory of Radar Signal Processing, School of Electronic Engineering, Xidian University, Xi'an
来源：
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2024年 / 53卷 / 04期
基金：
中国国家自然科学基金;
关键词：
autofocus; convolutional neural networks; phase error; SAR;
D O I：
10.11947/j.AGCS.2024.20230281
中图分类号：
学科分类号：
摘要：
Autofocus is a key technology for high-resolution synthetic aperture radar imaging. However, traditional SAR autofocus methods require too many iterations, have low computational efficiency, and are unsuitable for on-orbit processing. This paper proposes a fast SAR autofocus method based on convolutional neural networks. This method utilizes CNNs to learn the mapping from defocused images to focused images, mainly designed to correct the azimuth phase errors. It has a real-time performance and is more suitable for on-orbit processing since it does not need to iterate or adjust parameters in the testing phase. Experimental results on real SAR data show that our proposed method has the highest focusing quality and speed. © 2024 SinoMaps Press. All rights reserved.
引用
收藏
页码：610 / 619
页数：9
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