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.