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一种改进的高光谱解混非负矩阵分解初始化方法

Improved Hyperspectral Unmixed Initialization Method Based on Non-Negative Matrix Factorization

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摘要

提出了一种结合欧氏距离和光谱信息散度的改进的高光谱解混非负矩阵分解(NMF)初始化方法(IISSF)。在初始化基础上,结合标准NMF算法和分块NMF算法进行平行对比实验。结果表明,在合成影像实验中,在信噪比为20 dB~50 dB范围内,经过IISSF初始化后的分块NMF算法获取的结果要优于其他方法;且其在真实影像实验中获取的端元光谱与真实影像端元光谱之间具有最小的平均光谱角差值,即0.1812 ;其重构影像与真实影像之间的均方根误差值最小,为0.007。

Abstract

An improved hyperspectral unmixed initialization method (IISSF) based on non-negative matrix factorization (NMF) combining Euclidean distance and spectral information divergence is proposed. On the basis of initialization, a parallel comparison experiment is performed in combination with the standard NMF algorithm and the block NMF algorithm. The results show that, in the synthetic image experiment, the block NMF algorithm after IISSF initialization is better than other methods in the signal-to-noise ratio range from 20 dB to 50 dB. There is a minimum average spectral angular difference between the end-member spectrum obtained in the real image experiment and the reality image endmember spectra, i.e., 0.1812. The root mean square error between the reconstructed image and the real image is the smallest, i.e., 0.007.

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中图分类号:TP751.1

DOI:10.3788/LOP57.061020

所属栏目:图像处理

基金项目:国家重点研发计划 、国家自然科学基金、河南省水利科技攻关计划项目;

收稿日期:2019-09-10

修改稿日期:2018-11-19

网络出版日期:2020-03-01

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黄鹏飞:河南理工大学测绘与国土信息工程学院, 河南 焦作 454150黄河水利科学研究院, 河南 郑州 450000
孔祥兵:黄河水利科学研究院, 河南 郑州 450000
景海涛:河南理工大学测绘与国土信息工程学院, 河南 焦作 454150

联系人作者:孔祥兵(kongxb_whu@foxmail.com)

备注:国家重点研发计划 、国家自然科学基金、河南省水利科技攻关计划项目;

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引用该论文

Huang Pengfei,Kong Xiangbing,Jing Haitao. Improved Hyperspectral Unmixed Initialization Method Based on Non-Negative Matrix Factorization[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061020

黄鹏飞,孔祥兵,景海涛. 一种改进的高光谱解混非负矩阵分解初始化方法[J]. 激光与光电子学进展, 2020, 57(6): 061020

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