Dimensionality Reduction of Hyperspectral Data Based on ISOMAP Algorithm

摘要：

In this paper, a new manifold learning method to reduce the dimension of hyperspectral data is proposed. In this method, ISOMAP algorithm is used to extract the inherent manifold of hyperspectral data to transform the high-dimensional space into a low-dimensional space. Experiments show that the method is effective, meaningful, and provides a new way for reducing the dimension of hyperspectral data while expands the application area of manifold learning in the hyperspectral data processing filed.

关键词： Manifold Learning Dimensionality Reduction

作者: Dong Guangjun Zhang Yongsheng Ji Song

作者单位: Information Engineering University Institute of Surveying and Mapping,Zhengzhou 450052 China

会议类型: 国际会议

会议名称: 第八届国际电子测量与仪器学术会议(Proceedings of 2007 8th International Conference on Electronic Measurement & Instruments)

会议地点: 西安

会议语种:英文

在线出版日期: 2007-08-16（万方平台首次上网日期，不代表论文的发表时间）