Hyperspectral remote sensing is a frontier technology that is currently being investigated by researchers and scientists with regard to the detection and identification of minerals, vegetation, crops, ocean and other backgrounds. Hyperspectral remote sensing imagery has tens or even several hundreds of bands with bandwidth of nanometer level and contains abundant spectral information, but its redundancy made by mass data and high correlation in adjacent bands restricts the application of hyperspectral remote sensing. So, rational feature extraction will be required to find the optimal characteristics for maximum separability before the hyperspectral imagery classification. This thesis, taking Hyperion hyperspectral visible/infrared sensor aboard an earth observation satellite platform (EO-1) for example, applied independent component analysis (ICA) to carry on the feature extraction on Hyperion hyperspectral imagery with high dimensions, a strong correlation and huge data. Then, a modified independent component analysis algorithm (M-ICA) was put forward. Finally, the results by the algorithm (M-ICA) and principal component analysis (PCA) were compared with experimental data of selected test area, while using the maximum likelihood classification method to classify objects to verify its validity on object type extraction. Experimental results show that the operation efficiency of M-ICA algorithm is apparently higher than the traditional FastICA algorithm, which average iteration number and time are only 14.49% and 17.32% of the latter. In addition, the total classification accuracy of M-ICA result image is up to 90.57%, which is raised 10.8% and 20.0% more than that of PCA result image and that of Original Hyperion imagery. Therefore, the results indicate that M-ICA is a good feature extraction method with fast convergence speed, can effectively realize the dimension reduction of the hyperspectral imagery, and improve the objects separability in different categories.