Taking Huangfengqiao forest farm as the studied object, which is located in Youxian county, Zhuzhou City, Hunan Province, the forest information was extracted from hyper-spectral remote sensing image by using 7 kinds of classification method such as minimum distance.Mahalanobis distance, maximum likelihood,spectral angle mapping, spectral information divergence, neural network, support vector machine. Comparing the results of information extraction shows that among 7 kinds of methods of Hyperion image information extraction, support vector machine, neural network and mahalanobis distance were more suitable for forest information extraction,with overall accuracy of 67.39%,66.30%,62.68% respectively. The effect of maximum likelihood method extracted the coniferous forest information was batter than other's with an accuracy of 67.31%. The support vector machine was most suitable for extracting the information of broad-leaved forest, with an accuracy of 80.19%, the accuracy was much higher than the other six. Mahalanobis distance and neural network mthods were the two maximam ways to extract the bamboo forest information,with accuracy of 84.21% and 81.58% respectively.%以湖南省株洲市攸县黄丰桥林场为研究对象,运用最小距离、马氏距离、最大似然、光谱角制图、光谱信息散度、神经网络、支持向量机7种分类方法对Hyperion高光谱数据进行森林信息提取.通过对信息提取结果进行比较分析得出:针对Hyperion影像信息提取的7种方法中,支持向量机、神经网络和马氏距离3种分类方法较适合森林信息的提取,总体精度分别为67.39％、66.30％、62.68％;最大似然法在针叶林信息提取中的效果较好,其精度为67.31％;支持向量机法最适合提取阔叶林信息,其精度为80.19％,远远高于其它6种分类方法；运用马氏距离和神经网络法提取竹林信息精度最高,分别达到了84.21％和81.58％.
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