[1]贾志成,郑笑,郭艳菊,等.改进鲸群优化子空间匹配追踪的稀疏解混算法[J].深圳大学学报理工版,2020,37(1):63-71.[doi:10.3724/SP.J.1249.2020.01063]
　JIA Zhicheng,ZHENG Xiao,GUO Yanju,et al.Sparse unmixing using the improved whale optimized subspace matching pursuit algorithm[J].Journal of Shenzhen University Science and Engineering,2020,37(1):63-71.[doi:10.3724/SP.J.1249.2020.01063]
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改进鲸群优化子空间匹配追踪的稀疏解混算法()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第37卷
期数:
2020年第1期
页码:
63-71
栏目:
电子与信息科学
出版日期:
2020-01-08

文章信息/Info

Title:
Sparse unmixing using the improved whale optimized subspace matching pursuit algorithm
文章编号:
202001010
作者:
贾志成1; 郑笑1; 郭艳菊1; 陈雷2;  3
1) 河北工业大学电子信息工程学院，天津 300401
2) 天津大学精密仪器与光电子工程学院，天津 300072
3) 天津商业大学信息工程学院，天津 300134
Author(s):
JIA Zhicheng1;  ZHENG Xiao1;  GUO Yanju1;  and CHEN Lei2;  3
1) School of Electronic Information Engineering, Hebei University of Technology, Tianjin 300401, P.R.China
2) School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, P.R.China
3) School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, P.R.China
关键词:
计算机图像处理; 高光谱图像; 稀疏解混; 子空间匹配追踪算法; 冗余端元; 仿生智能优化; 鲸群优化算法
Keywords:
computer image processing;  hyperspectral images;  sparse unmixing;  subspace matching pursuit algorithm;  redundant endmembers;  swarm intelligence optimization;  whale optimization algorithm
分类号:
TN911.7
DOI:
10.3724/SP.J.1249.2020.01063
文献标志码:
A
摘要:
为了提升高光谱图像的稀疏解混精度，提出一种基于改进鲸群优化算法的子空间匹配追踪（improved whale optimized subspace matching pursuit, IWOSMP）的稀疏解混算法．对鲸群优化算法进行改进，通过引入非线性种群控制参数和进化策略，提高鲸群优化算法的收敛速度和收敛精度，基于子空间匹配追踪算法，以约束稀疏回归为目标函数，利用改进的鲸群优化算法对已知端元集求解丰度系数．以重构误差最小为标准，通过最大程度地去除系数较小的冗余端元，提高子空间匹配追踪算法的端元提取的精确度，进一步提高了高光谱图像的解混精度．合成图像实验和真实遥感图像实验表明，IWOSMP能有效去除大量的冗余端元，且解混精度更高．
Abstract:
This paper proposes a whale optimized subspace matching pursuit algorithm to improve the precision of sparse unmixing of hyperspectral data. The proposed algorithm modifies the traditional whale group optimization algorithm by introducing the nonlinear population control parameters and evolution strategy and thus improves the convergence speed and convergence precision of the whale cluster optimization algorithm. Based on the improved subspace matching pursuit algorithm, the new algorithm uses the whale optimization algorithm to solve the abundance coefficients of the known end members, where the objective function is modeled as a constrained sparse regression. In order to improve the precision of the subspace matching pursuit algorithm, the minimum reconstruction error is taken as the criterion and the redundant end members with smaller coefficients are removed to the greatest extent. The simulation and real data experiments show that the proposed algorithm can effectively remove the redundant endmembers and has a higher precision of unmixing.

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备注/Memo

备注/Memo:

Received：2019-02-27；Accepted：2019-04-09
Foundation：National Nature Science Foundation of China (61401307)；China Postdoctoral Science Foundation (2014M561184)；Tianjin Research Program of Application Foundation and Advanced Technology (15JCYBJC17100)
Corresponding author：Professor JIA Zhicheng. E-mail: jiazc@hebut.edu.cn.Lecture GUO Yanju. E-mail: guoyanju@163.com

Citation：JIA Zhicheng, ZHENG Xiao, GUO Yanju, et al. Sparse unmixing using the improved whale optimized subspace matching pursuit algorithm［J］. Journal of Shenzhen University Science and Engineering, 2020, 37(1): 63-71.（in Chinese）
基金项目：国家自然科学基金资助项目（61401307）；中国博士后科学基金资助项目（2014M561184）；天津市应用基础与前沿技术研究计划资助项目（15JCYBJC17100）
作者简介：贾志成（1957—），河北工业大学教授．研究方向：高光谱图像处理与仿生智能计算．E-mail: jiazc@hebut.edu.cn
郑笑(1993—), 河北工业大学硕士研究生．研究方向： 高光谱图像处理与仿生智能计算．
E-mail: zhengxiao_606@163.com
贾志成、郑 笑为共同第一作者．
引文：贾志成，郑笑，郭艳菊，等．改进鲸群优化子空间匹配追踪的稀疏解混算法［J］. 深圳大学学报理工版，2020，37(1)：63-71.

更新日期/Last Update: 2020-01-30