Supervised Linear Manifold Learning Feature Extraction for Hyperspectral Image Classification

摘要：

A supervised neighborhood preserving embedding (SNPE) linear manifold learning feature extraction method for hyperspectral image classification is presented in this paper.A points k nearest neighbours are found by using new distance which is proposed according to prior class-label information.The new distance makes intra-class more tightly and interclass more seperately.SNPE overcomes the single manifold assumption of NPE.Data sets laid on (or near) multiple manifolds can be processed.Experiments on AVIRIS hyperspectral data sets demonstrate the effectiveness of our method.

关键词： feature extraction dimensionality reduction neighborhood preserving embedding manifold learning hyperspectral image classification

作者: Jinhuan Wen Zheng Tian

作者单位: School of science,Northwestern Polytechnical University Xian,China Department of Computer Science and Engineering,Northwestern Polytechnical University Xian,China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer and Network Technology(ICCNT 2011)(2011第三届IEEE计算机与网络技术国际会议)

会议地点: 太原

会议语种:英文

页码: 534-537

在线出版日期: 2011-02-26（万方平台首次上网日期，不代表论文的发表时间）