稀疏非负矩阵分解下的遥感图像融合

doi:10.3969/j.issn.1001-2400.2016.02.033

西安电子科技大学学报 ›› 2016, Vol. 43 ›› Issue (2): 193-198.doi: 10.3969/j.issn.1001-2400.2016.02.033

稀疏非负矩阵分解下的遥感图像融合

李红1,2,3;刘芳1,2,3;张凯2,3

(1. 西安电子科技大学计算机学院，陕西西安 710071;
2. 西安电子科技大学智能感知与图像理解教育部重点实验室，陕西西安 710071;
3. 西安电子科技大学国际智能感知与计算联合研究中心，陕西西安 710071)

收稿日期:2015-06-26 出版日期:2016-04-20 发布日期:2016-05-27
通讯作者: 李红
作者简介:李红(1976-)，女，西安电子科技大学博士研究生，E-mail: honglishining@163.com．
基金资助:
国家重点基础研究发展计划资助项目(2013CB329402)；国家自然科学基金资助项目(61173090，61573267，91438103，91438201)；高等学校学科创新引智计划资助项目(B07048)

Remote sensing image fusion based on sparse non-negative matrix factorization

LI Hong1,2,3;LIU Fang1,2,3;ZHANG Kai2,3

(1. 西安电子科技大学计算机学院，陕西西安 710071;
2. 西安电子科技大学智能感知与图像理解教育部重点实验室，陕西西安 710071;
3. 西安电子科技大学国际智能感知与计算联合研究中心，陕西西安 710071)

Received:2015-06-26 Online:2016-04-20 Published:2016-05-27
Contact: LI Hong

摘要/Abstract

摘要：

为了降低多光谱图像与全色图像融合过程中的光谱扭曲和空间失真，提出了一种稀疏非负矩阵分解的融合新方法．首先从全色图像学习出一个高分辨字典和相应的低分辨字典，然后构造多光谱图像的稀疏非负矩阵分解模型，在低分辨字典下获得光谱系数矩阵，最后将该系数矩阵与高分辨字典相乘得到融合后的高分辨多光谱图像．稀疏正则项的引入有效克服了标准非负矩阵分解算法的不稳定现象，能够较好地保持图像的光谱信息和空间信息．将该方法应用于快鸟卫星和地球眼卫星数据，与同类方法的对比分析结果显示:该方法能够减少光谱扭曲和空间信息的损失，得到的融合结果在视觉效果和客观评价指标上均优于对比方法．

关键词: 遥感图像融合, 非负矩阵分解, 稀疏正则

Abstract:

In order to reduce the spectral and spatial distortions, a novel method based on sparse non-negative matrix factorization (SNMF) is proposed for multispectral and panchromatic images fusion. Firstly, the high spatial resolution and low spatial resolution dictionaries are learned from panchromatic. Then we construct a sparse non-negative matrix factorization model of the multispectral image. Thus, the coefficients matrix with spectral information can be obtained. The high spatial resolution multispectral image is produced by the multiplication high spatial resolution dictionary and the coefficients matrix. By introducing the sparse regularization, the instability of the standard non-negative matrix factorization is conquered and the fused image can preserve the high spectral and spatial information. Some experiments are made on QuickBird and Geoeye satellite datasets, and experimental results show that our proposed method can reduce distortions in both the spectral and spatial domains, and outperform some related pan-sharpening approaches in visual results and numerical guidelines.

Key words: remote sensing image fusion, non-negative matrix factorization, sparse regularization

李红;刘芳;张凯. 稀疏非负矩阵分解下的遥感图像融合[J]. 西安电子科技大学学报, 2016, 43(2): 193-198.

LI Hong;LIU Fang;ZHANG Kai. Remote sensing image fusion based on sparse non-negative matrix factorization[J]. Journal of Xidian University, 2016, 43(2): 193-198.

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稀疏非负矩阵分解下的遥感图像融合

Remote sensing image fusion based on sparse non-negative matrix factorization

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