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遥感影像数据的大气校正是高光谱遥感地空对比、信息提取的前提和关键,如何根据不同数据、不同研究区、不同研究目的选择合适的大气校正方法是高光谱遥感应用研究的重点和难点。针对EO-1卫星Hyperion高光谱遥感数据特点和研究区地形环境特征,分别选择线性回归经验模型、基于MODTRAN4模型的FLAASH和基于DEM数据的ACORN-3模型不同大气校正方法对研究区Hyperion数据进行大气校正。从波谱匹配、识别的目的出发,通过计算不同方法校正后影像像元的波谱曲线与实测地面波谱曲线的匹配程度分析不同大气校正方法的校正效果。“,”The atmospheric correction for hyperspectral remote sensing images is absolutely necessary for improving the precision of data pre-processing.The emphasis and difficulty is how to select the appropriate method of atmospheric correction for hyperspectral remote sensing based on different data,different research areas,and different research purposes.In this paper,logarithm residual model,empirical linear regression model,FLAASH model and ACORN-3 model are used as the atmospheric correction models.The Hyperion hyperspectral image is used as the original data to analyze the precision of atmospheric correction.In order to evaluate and validate the correction results,we use the object spectral response curves and spectral matching to analyze the original images and the images after atmospheric correction.As a result,the reflectance of ground object by the method in the paper can eliminate the atmospheric scatter influence effectively and the edges of the surface objects are distinct and easy to identify.Furthermore,the ACORN-3 atmospheric correction model is better to Hyperion hyperspectral remote sensing images than the other model.