Empirical study on sparse representation model of SAR images

HUANG Kemeng; JIANG Nana; ZHAO Wenbo; ZHENG Yanxin; LIU Wenping; ZHU Jubo

doi:10.13471/j.cnki.acta.snus.ZR20230037

您当前的位置：

首页 >

文章列表页 >

Empirical study on sparse representation model of SAR images

Research articles | 更新时间：2024-09-29

- Empirical study on sparse representation model of SAR images
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 63, Issue 4, Pages: 107-114(2024)
- 作者机构：
  
  中山大学人工智能学院，广东珠海 519082
- 作者简介：
- 基金信息：
- DOI：10.13471/j.cnki.acta.snus.ZR20230037
  CLC： TN95
- Published：25 July 2024，
  
  Published Online：01 April 2024，
  
  Received：08 December 2023，
  
  Accepted：24 January 2024
- 稿件说明：
移动端阅览
黄柯蒙,姜娜娜,赵文博等.SAR图像稀疏表示模型的实证研究[J].中山大学学报(自然科学版)(中英文),2024,63(04):107-114.

HUANG Kemeng,JIANG Nana,ZHAO Wenbo,et al.Empirical study on sparse representation model of SAR images[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(04):107-114.
黄柯蒙,姜娜娜,赵文博等.SAR图像稀疏表示模型的实证研究[J].中山大学学报(自然科学版)(中英文),2024,63(04):107-114. DOI： 10.13471/j.cnki.acta.snus.ZR20230037.

HUANG Kemeng,JIANG Nana,ZHAO Wenbo,et al.Empirical study on sparse representation model of SAR images[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(04):107-114. DOI： 10.13471/j.cnki.acta.snus.ZR20230037.

摘要

对合成孔径雷达（SAR）场景使用稀疏表示算法得到基函数字典示例，滤波前后图像稀疏表示的对比研究表明：相干斑噪声对SAR场景稀疏表示的字典结果有影响。选取浦江二号、ALOS2和SIR-C的特定SAR图像数据，通过设置单因素条件探讨优化算法、样本内容、数据集大小、雷达分辨率、极化方式、波段对字典结果的影响。结果表明：1）SAR场景稀疏表示学习出的字典和雷达波段、分辨率、极化方式有关，和所选取的不同样本内容、数据集的大小以及所使用的优化算法无关。2）C波段比L波段更能反应SAR场景的稀疏性。3）降采样数据集更能反应SAR场景的稀疏性。4）HH、VV极化图像学习出的字典更具有本质特征。

Abstract

An example of using the sparse representation algorithm to obtain a basis function dictionary for synthetic-aperture radar （SAR） scenes. The comparative of sparse representation of images before and after filtering shows that speckle noise has an impact on the dictionary results of sparse representation of SAR scenes. Select specific SAR image data from Pujiang No.2， ALOS2， and SIR-C， it is discussed that the effects of optimization algorithm， sample content， dataset size， radar resolution， polarization method， and band on dictionary results by setting single factor conditions. The results show that：（1） The dictionary learned from sparse representation of SAR scenes is related to radar band， resolution and polarization mode， and is independent of sample contents， datasets size， and optimization algorithms. （2）C-band can better reflect the sparsity of SAR scenes than L band. （3）The downsampling dataset can better reflect the sparsity of SAR scenes. （4）The dictionaries learned from HH and VV polarized images have more essential features.

关键词

SAR稀疏表示基函数字典相干斑噪声极化

Keywords

SARsparse representationbase function dictionaryspeckle noisepolarization

references

郭华东，邵芸，王长林，等，2000. 雷达对地观测理论与应用［M］. 北京：科学出版社：348-421．

匡纲要， 2011. 极化合成孔径雷达基础理论及其应用［M］. 长沙：国防科技大学出版社：1-20.

黎兰， 2014. 基于稀疏表示的SAR目标识别算法研究［D］. 成都：电子科技大学.

梁彦， 2019. 最优估计理论与应用：最小二乘估计［M］. 西安：西北工业大学出版社：1-17.

皮亦鸣，杨建宇，付毓生，等， 2007. 合成孔径雷达成像原理［M］. 成都：电子科技大学出版社.

齐尔， 2014. 合成孔径雷达极化理论及应用［M］. 北京：国防工业出版社：19-62.

王力哲，刘鹏， 2022. 遥感图像质量提升理论与方法［M］. 北京：科学出版社：20-37.

CETIN M， KARL W C， 2001. Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization［J］.IEEE Trans Image Process，10（4）：623-631.

CHEN S S， DONOHO D L， SAUNDERS M A， 1999. Atomic decomposition by basis pursuit［J］. J Sci Comput，20（1）： 33-61.

DONOHO D L， 2006. Compressed sensing［J］. IEEE Trans Inf Theory， 52（4）：1289-1306.

ELAD M， 2010 . Sparse and redundant representations［M］. New York： Springer.

FIELD D J， 1987. Relations between the statistics of natural images and the response properties of cortical cells［J］. J Opt Soc Am A，4（12）：2379-2394.

KIM S J， KOH K， LUSTIG M， et al，2007. An interior-point method for large-scale l1-regularized least squares［J］. J Sel Top Signal Process， 1（4）：606-617 .

LEE J S， 1980. Digital image enhancement and noise filtering by use of local statistics［J］. IEEE Trans Pattern Anal Mach Intell， 2： 165-168.

MALLAT S G， ZHANG Z， 1993. Matching pursuits with time frequency dictionaries［J］. IEEE Trans Signal Process，41（12）： 3397-3415.

OLSHAUSEN B A， FIELD D J， 1996. Emergence of simple-cell receptive field properties by learning a sparse code for natural images［J］. Nature，381（6583）： 607-609.

PATI Y C， REZAIIFAR R， KRISHNAPRASAD P， 1993. Orthogonal matching pursuit： Recursive function approximation with applications to wavelet decomposition［C］// The 27th Annual Asilomar Conference on Signals， Systems and Computers. IEEE： 40-44.

POTTER L C， ERTIN E， MOSE R L， 2010. Sparsity and compressed sensing in radar imaging［J］. Proc IEEE， 98（6）： 1006-1020.

WRIGHT J， YANG A， MA Y，et al， 2009. Robust face recognition via sparse representation［J］. IEEE Trans Pattern Anal Mach Intell， 31（2）： 210-227.

YANG J C， WRIGHT J， HUANG T，et al， 2008. Image super-resolution as sparse representation of raw image patches［C］//IEEE Conference on Computer Vision and Pattern Recognition. IEEE： 1-8.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Responsibility to Optical Pumping for Rb Isotope with Magnetic Resonance

Fusion method based on shearlet transform and sparse representation

Related Author

LI Chaorui

LIU Xiaowei

CHANG Lihong

Related Institution

中山大学物理科学与工程技术学院

Institute of Mathematics and Information Science, Baoji University of Arts and Sciences

Postal code：510275
Tel：020-84112585，84113223 Email：xuebaozr@mail.sysu.edu.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰