Hyperspectral feature recognition based on kernel PCA and relational perspective map

• 来源期刊: 中国光学快报(英文版)
• 学科: 工学
• 关键词: 高光谱数据 特征识别 PCA 地图 核主成分分析 数据降维 二维空间 发现规律
• 年，卷（期）: 2010,（8）
• 所属期刊栏目: Remote Sensing and Sensors
• 页码范围: 811-814
• 页数: 4页
• 分类号: TP79 TP391.72
• 语种: 英文

摘要

A novel joint kernel principal component analysis(PCA) and relational perspective map(RPM) method called KPmapper is proposed for hyperspectral dimensionality reduction and spectral feature recognition. Kernel PCA is used to analyze hyperspectral data so that the major information corresponding to features can be better extracted.RPM is used to visualize hyperspectral data through two-dimensional(2D) maps, and it is an efficient approach to discover regularities and extract information by partitioning the data into pieces and mapping them onto a 2D space.The experimental results prove that the KPmapper algorithm can effectively obtain the intrinsic features in nonlinear high dimensional data.It is useful and impressing for dimensionality reduction and spectral feature recognition.