Structured measurement matrix by particle swarm optimization for remote sensing compressive imaging

Hui-feng TAO; Xing YANG; Jie CHEN; Yong-shun LING; Song-feng YIN

doi:10.3788/OPE.20162411.2821

您当前的位置：

首页 >

文章列表页 >

Structured measurement matrix by particle swarm optimization for remote sensing compressive imaging

Information science | 更新时间：2020-07-07

- Structured measurement matrix by particle swarm optimization for remote sensing compressive imaging
- Optics and Precision Engineering Vol. 24, Issue 11, Pages: 2821-2829(2016)
- 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：
- 基金信息：
- DOI：10.3788/OPE.20162411.2821
  CLC： TP751
- Received：14 July 2016，
  
  Accepted：09 September 2016，
  
  Published：25 November 2016
- 稿件说明：
移动端阅览
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：

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

references

严奉霞,朱炬波,刘吉英,等. 光学遥感压缩成像技术[J]. 航天返回与遥感, 2014, 35(1):54-62,96.

YAN F X, ZHU J B, LIU J Y, et al.. Compressive imaging techniques in optical remote sensing[J]. Spacecraft Recovery & Remote Sensing, 2014, 35(1):54-62,96.(in Chinese)

范晋祥,岳艳军. 军用红外成像系统新概念新体制的发展[J]. 红外与激光工程,2011, 40(1):1-6.

FAN J X, YUE Y J. Development in new concepts and new schemes for military infrared imaging systems[J].Infrared and Laser Engineering, 2011, 40(1):1-6.(in Chinese)

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

陈涛,李正炜,王建立,等. 应用压缩传感理论的单像素相机成像系统[J]. 光学精密工程, 2012, 20(11):2523-2530.

CHEN T, LI ZH W, WANG J L, et al.. Imaging system of single pixel camera based on compressed sensing[J]. Opt. Precision Eng., 2012, 20(11):2523-2530.(in Chinese)

王朋,荣志斌,何俊华,等. 基于压缩感知的偏振光成像技术研究[J]. 红外与激光工程,2016, 45(2):0228005.

WANG P, RONG ZH B, HE J H, et al.. Polarization imaging based on compressed sensing theory[J]. Infrared and Laser Engineering,2016, 45(2):0228005.(in Chinese)

MARCIA R F, HARMANY Z T, WILLETT R M. Compressive coded aperture imaging[C]. Proceeding of SPIE, 2009, 72460G.

王忠良,冯燕,肖华,等. 高光谱图像的分布式压缩感知成像与重构[J]. 光学精密工程, 2015, 23(4):1131-1137.

WANG ZH L, FENG Y, XIAO H, et al.. Distributed compressive sensing imaging and reconstruction of hyperspectral imagery[J]. Opt. Precision Eng., 2015, 23(4):1131-1137.(in Chinese)

CANDES E. The restricted isometry property and its implications for compressed sensing[J]. Comptes Rendus Mathematique, 2008, 346(9):589-592.

ELAD M. Optimized projections for compressed sensing[J]. IEEE Transactions on Signal Processing, 2007, 55(12):5695-5703.

ABOLGHASEMI V, FERDOWSI S, SANEI S. A gradient-based alternating minimization approach for optimization of the measurement matrix in compressive sensing[J]. Signal Processing, 2012, 92(3):999-1009.

郑红,李振,黄盈. 一种基于拟牛顿法的CS投影矩阵优化算法[J]. 电子学报,2014, 42(10):1977-1982.

ZHENG H, LI ZH, HUANG Y. An optimization method for CS projection matrix based on quasi-Newton method[J]. Acta Electronica Sinica,2014, 42(10):1977-1982.(in Chinese)

RAMIREZ A, ARGUELLO H, ARCE G R, et al.. Spectral image classification from optimal coded-aperture compressive measurements[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(6):3299-3309.

RAMIREZ A, ARCE G R, SADLER B M. Spectral image unmixing from optimal coded-aperture compressive measurements[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1):405-415.

郭静波,汪韧. 交替寻优生成元素幅值结合混沌随机相位构造循环测量矩阵[J]. 物理学报,2015, 64(13):1-12.

GUO J B, WANG R. Constructing circulant measurement matrix through alternating optimizing amplitudes together with chaotic stochastic phases of the matrix generating elements[J]. Acta Physica Sinica, 2015, 64(13):1-12.(in Chinese)

KITTLE D, CHOI K, WAGADARIKAR A, et al.. Multiframe image estimation for coded aperture snapshot spectral imagers[J]. Appl. Opt., 2010, 49(36):6824-6833.

STROHMER T, HEATH R W. Grassmannian frames with applications to coding and communication[J]. Applied and Computational Harmonic Analysis, 2003, 14(3):257-275.

WELCH L. Lower bounds on the maximum cross correlation of signals[J]. IEEE Transactions on Information Theory, 1974, 20(3):397-399.

KENNEDY J, EBERⅡART R C. Particle swarm optimization[C]. IEEE International Conference on Neural Networks, 1995, 4:1942-1948.

ZHENG Q, FAN Y. Adaptive inertia weight particle swarm optimization[C]. ICAISC, 2006, 4029:450-459.

Views

427

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Spatial adaptation and frequency fusion network for single remote sensing image super-resolution

Iterative reconstruction of compressive sensing combining image hierarchical-feature

Passive compressive ghost imaging with low rank clustering

Generate adversarial network for super-resolution reconstruction of remote sensing images by fusing edge enhancement and non-local modules

Fast extraction of buildings from remote sensing images by fusion of CNN and Transformer

Related Author

YANG Yichuan

MA Zhongqi

ZHOU Xinyao

ZHENG Fujian

HUANG Hong

LIU Yuhong

YANG Heng

LEI Teng

Related Institution

Beijing Institute of Space Machinery and Electronics

Key Laboratory of Optoelectronic Technology and System， Ministry of Education， Chongqing University

School of Electronic and Information Engineering， Lanzhou Jiaotong University

Shanghai Institute of Technical Physics， Chinese Academy of Sciences

University of Chinese Academy of Sciences

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 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