粒子群优化结构测量矩阵的遥感压缩成像

陶会锋; 杨星; 陈杰; 凌永顺; 殷松峰

doi:10.3788/OPE.20162411.2821

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粒子群优化结构测量矩阵的遥感压缩成像

信息科学 | 更新时间：2020-07-07

- 粒子群优化结构测量矩阵的遥感压缩成像
- Structured measurement matrix by particle swarm optimization for remote sensing compressive imaging
- 光学精密工程;Editorial Office of Optics and Precision Engineeri 2016年24卷第11期页码：2821-2829
- 作者机构：
  
  1.电子工程学院脉冲功率激光技术国家重点实验室, 安徽合肥 230037
  2.电子工程学院红外与低温等离子体安徽省重点实验室, 安徽合肥 230037
  3.安徽建筑大学电子与信息工程学院, 安徽合肥 230601
- 作者简介：
  
  陶会锋(1988-),男,河北邯郸人,博士研究生, 2011年、2014年于电子工程学院分别获得学士、硕士学位,主要从事光学信号获取与处理方面的研究. E-mail:taohfeei@163.com. E-mail:taohfeei@163.com.
  [ "杨星(1983-),男,四川都江堰人,助理研究员,2006年、2012年于电子工程学院分别获得学士、博士学位,主要从事模式识别和人工智能方面的研究。E-mail:yangxing1983@163.com" ]
  [ "凌永顺(1937-),男,安徽定远人,研究员,院士,1960年于北京师范大学获得学士学位,主要从事光学工程方面的研究。E-mail:Lyseei@163.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(No.61503394);安徽省自然科学基金资助项目(No.1408085QF131，No.1508085QF121);安徽高等学校自然科学研究项目(No.KJ2015ZD14，No.KJ2016A149)
- DOI：10.3788/OPE.20162411.2821
  中图分类号： TP751
- 收稿日期：2016-07-14，
  
  录用日期：2016-9-9，
  
  纸质出版日期：2016-11-25
- 稿件说明：
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陶会锋, 杨星, 陈杰, 等. 粒子群优化结构测量矩阵的遥感压缩成像[J]. 光学精密工程;Editorial Office of Optics and Precision Engineeri, 2016,24(11):2821-2829.

Hui-feng TAO, Xing YANG, Jie CHEN, et al. Structured measurement matrix by particle swarm optimization for remote sensing compressive imaging[J]. Optics and precision engineering, 2016, 24(11): 2821-2829.
陶会锋, 杨星, 陈杰, 等. 粒子群优化结构测量矩阵的遥感压缩成像[J]. 光学精密工程;Editorial Office of Optics and Precision Engineeri, 2016,24(11):2821-2829. DOI： 10.3788/OPE.20162411.2821.

Hui-feng TAO, Xing YANG, Jie CHEN, et al. Structured measurement matrix by particle swarm optimization for remote sensing compressive imaging[J]. Optics and precision engineering, 2016, 24(11): 2821-2829. DOI： 10.3788/OPE.20162411.2821.

摘要

针对块循环测量矩阵应用于遥感压缩成像存在图像重构性能不理想的问题，本文把粒子群智能优化算法引入到块循环矩阵优化中，实现了在保持矩阵结构不变的同时对块循环矩阵的优化。首先以相关系数的Welch界为阈值约束Gram矩阵非对角元素构造目标矩阵；然后以Gram矩阵逼近目标矩阵的方式建立目标函数，将优化对象改为构造块循环矩阵的自由元向量。为提高优化效率，文中采用权重自适应更新的方式提高粒子搜索能力。开展了相关重构对比实验，结果表明，优化后的块循环测量矩阵在保持矩阵结构的同时，降低了与稀疏变换矩阵的相关性，其与稀疏变换矩阵的最大相关系数、平均相关系数和阈值平均相关系数分别降低了0.027 3、0.017 5和0.004 6，得到的结果显示优化的块循环矩阵提高了图像的重构性能。

Abstract

For non-ideal image construction performance of a block circulant matrix in remote sensing compressive imaging

this paper introduces the particle swarm optimization intelligent algorithm into optimizing the block circulant matrix

meanwhile maintaining the matrix structure. Firstly

the Welch bound of a correlation coefficient is taken as a threshold value to restrain the off-diagonal entries of the Gram matrix and to build a target matrix. Then

the objective function is established by making the Gram matrix approach the target matrix

and the optimized variable is replaced as the free entries to compose the block circulant matrix. To improve the optimized efficiency

the weight adaptive update is used to improve the partical search capacity. A construction comparison experiment is carried out

the results show that the correlation properties of the block circulant matrix with the sparse transform matrix has been reduced while maintaining the matrix structure

and the coefficients for maximum correlation

average correction and threshold average correction have been reduced by 0.027 3

0.017 5 and 0.004 6

respectively. These results show the image construction performance is improved by optimized block circulant matrix.

关键词

Keywords

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