Research on Dimensionality Reduction Technology of Hyperspectral Data

WANG Xuhong; XIAOping; GUO Jianming

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Research on Dimensionality Reduction Technology of Hyperspectral Data

- Research on Dimensionality Reduction Technology of Hyperspectral Data
- Bulletin of Soiland Water Conservation Vol. 25, Issue 6, Pages: 89-91(2006)
- 作者机构：
  
  西北大学城市资源学系,陕西,西安,710069
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2006
- 稿件说明：
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WANG Xuhong, XIAOping, GUO Jianming. Research on Dimensionality Reduction Technology of Hyperspectral Data[J]. Bulletin of Soiland Water Conservation, 2006, 25(6): 89-91.
DOI：

WANG Xuhong, XIAOping, GUO Jianming. Research on Dimensionality Reduction Technology of Hyperspectral Data[J]. Bulletin of Soiland Water Conservation, 2006, 25(6): 89-91. DOI：

摘要

高光谱数据对地物具有更高的光谱分辨率

但是由于高光谱数据巨大的数据量以及相邻波段之间的强相关性

导致了对这种数据的许多分类方法达不到应有的效果

从而在某种程度上制约了其广泛的应用。研究表明

特征提取的理论与方法对高光谱信息的优化处理是十分有效的。实验结果表明

在一定的分类精度范围内

减低维数而不丢失信息

可以提高分类器的效能

实现高维遥感数据的优化处理和高效利用。

Abstract

Hyperspect ral data have a high spect ral resolution for the object s of the earth. However

many analysis approaches of hyperspectral data do not provide a promising result because of it s great data volume and strong cor-relation between it s neighboring bands. Consequently

it rest rict s the efficiency and broad application of high reso-lution data. The research indicates that feature extraction is the highly effective theory and method to optimize hy-perspectral data and information. The result of experiment shows that with a given precision of classification

the reduction in dimensionality without loss of information improves the classifier performance

and helps to achieve the aims of optimal process and effective utilization of hyperspect ral remote sensing data.

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