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This letter introduces the averaged learning subspace method (ALSM) that can be applied directly to original hyperspectral data for the purpose of classifying land cover. The ALSM algorithm of classification consists of the following iterative steps: (1) generate the initial appropriate feature subspace for each class in training datasets using the class-featuring information compression method, and (2) update the subspaces according to the maximum projection principle. We compare ALSM with the support vector machine classifier. By conducting experiments on two hyperspectral datasets (48 bands and 191 bands, respectively), we demonstrate that the ALSM can make dimensional reduction and classification simultaneously. When compared with the SVM classifier, it appears that the ALSM can achieve a higher accuracy on classification in some cases.