基于小波包算法的压缩感知SAR成像方法

Q2 Physics and Astronomy 雷达学报 Pub Date : 2013-04-01 DOI:10.3724/SP.J.1300.2013.20068
Shi Yan, Di-rong Chen
{"title":"基于小波包算法的压缩感知SAR成像方法","authors":"Shi Yan, Di-rong Chen","doi":"10.3724/SP.J.1300.2013.20068","DOIUrl":null,"url":null,"abstract":"Compressive sensing SAR imaging can significantly reduce the sampling rate and the amount of data required, but it is essential only in the case where the reflection coefficients of a SAR scene are sparse. This paper proposes a compressive sensing SAR imaging method based on wavelet packet sparse representation. The wavelet packet algorithm is used to choose the most sparse representation of the SAR scene by training the same type of SAR images. By solving for the minimum 1 l norm optimization, the SAR scene reflection coefficients can be reconstructed. Unambiguous SAR images can be produced with the proposed method, even with fewer samples. SAR data simulation experiments demonstrate the efficiency of the proposed method.","PeriodicalId":37701,"journal":{"name":"雷达学报","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Compressive Sensing SAR Imaging Approach Based on Wavelet Package Algorithm\",\"authors\":\"Shi Yan, Di-rong Chen\",\"doi\":\"10.3724/SP.J.1300.2013.20068\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Compressive sensing SAR imaging can significantly reduce the sampling rate and the amount of data required, but it is essential only in the case where the reflection coefficients of a SAR scene are sparse. This paper proposes a compressive sensing SAR imaging method based on wavelet packet sparse representation. The wavelet packet algorithm is used to choose the most sparse representation of the SAR scene by training the same type of SAR images. By solving for the minimum 1 l norm optimization, the SAR scene reflection coefficients can be reconstructed. Unambiguous SAR images can be produced with the proposed method, even with fewer samples. SAR data simulation experiments demonstrate the efficiency of the proposed method.\",\"PeriodicalId\":37701,\"journal\":{\"name\":\"雷达学报\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"雷达学报\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.3724/SP.J.1300.2013.20068\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Physics and Astronomy\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"雷达学报","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.3724/SP.J.1300.2013.20068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Physics and Astronomy","Score":null,"Total":0}
引用次数: 0

摘要

压缩感知SAR成像可以显著降低采样率和所需的数据量,但只有在SAR场景反射系数稀疏的情况下才有必要。提出了一种基于小波包稀疏表示的压缩感知SAR成像方法。小波包算法通过训练相同类型的SAR图像,选择最稀疏的SAR场景表示。通过求解最小1 l范数优化,可以重建SAR场景反射系数。即使使用较少的样本,该方法也可以产生清晰的SAR图像。SAR数据仿真实验验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A Compressive Sensing SAR Imaging Approach Based on Wavelet Package Algorithm
Compressive sensing SAR imaging can significantly reduce the sampling rate and the amount of data required, but it is essential only in the case where the reflection coefficients of a SAR scene are sparse. This paper proposes a compressive sensing SAR imaging method based on wavelet packet sparse representation. The wavelet packet algorithm is used to choose the most sparse representation of the SAR scene by training the same type of SAR images. By solving for the minimum 1 l norm optimization, the SAR scene reflection coefficients can be reconstructed. Unambiguous SAR images can be produced with the proposed method, even with fewer samples. SAR data simulation experiments demonstrate the efficiency of the proposed method.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
雷达学报
雷达学报 Physics and Astronomy-Instrumentation
CiteScore
4.10
自引率
0.00%
发文量
882
期刊介绍: Information not localized
期刊最新文献
Integrated Chip Technologies for Microwave Photonics Distributed Multi-target Localization System Based on Optical Wavelength Division Multiplexing Network A Novel Cluster-Analysis Algorithm Based on MAP Framework for Multi-baseline InSAR Height Reconstruction A Dynamic and Adaptive Selection Radar Tracking Method Based on Information Entropy An Aircraft Detection Method Based on Convolutional Neural Networks in High-Resolution SAR Images
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1