摘要
基于最新的非线性降维方法——流形学习的理论,从高光谱遥感数据内在的非线性结构出发,采用全局化的等距映射(Isomap)方法进行降维,取得了优于常用的MNF方法的结果。把光谱角和光谱信息散度与测地距离相结合用于Isomap算法,结果在冗余方差和光谱规范化特征值方面优于采用传统欧氏距离计算邻域的Isomap方法。实验表明,流形学习是一种有效的高光谱遥感数据特征提取方法。
Manifold learning,as the novel nonlinear dimensionality reduction algorithm,is applied to dimensionality reduction and feature extraction of hyperspectral remote sensing information.In order to address inherent nonlinear characteristics of hyperspectral image,Isometric mapping（Isomap）,the most popular manifold learning algorithm,is employed to dimensionality reduction of hyperspectral image,and the experimental results show that it outperforms traditional MNF transform.In order to include spectral information into manifold learning,spectral angle（SA） and spectral information divergence（SID）,instead of Euclidean distance,are applied to derive the neighborhood distances in Isomap algorithm,and the result is better than that using Euclidean distance in terms of residual variance and normalized spectral eigenvalue.It is concluded that manifold learning is effective to dimensionality reduction and feature extraction from hyperspectral remote sensing imagery.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2011年第2期148-152,共5页
Geomatics and Information Science of Wuhan University
基金
高等学校博士学科点专项科研基金资助项目(20070290516)
国家自然科学基金资助项目(40871195)
国家教育部留学回国人员科研启动基金资助项目
关键词
高光谱遥感
降维
流形学习
ISOMAP算法
hyperspectral remote sensing
dimensionality reduction
manifold learning
Isomap
