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In recent years, local binary patterns (LBP) has been applied to hyperspectral remote sensing image classification because of its significant advantages in spatial feature extraction. Although the algorithm reduces the intra class variance in spatial feature extraction, it ignores the spectral features used to distinguish different ground objects. In order to avoid the problem of insufficient feature extraction and unsatisfactory classification effect caused by single feature extraction in the process of image classification, a new space spectrum feature vector is obtained by stacking spatial and spectral features. Then a new space spectrum feature vector is introduced into the kernel extreme learning machine, and a space spectrum feature kernel extreme learning machine (SS-KELM) is proposed. In order to verify the effectiveness of the proposed algorithm, two hyperspectral image data sets will be used for experiments. The experimental results show that the performance of the proposed SS-KELM algorithm is better than the traditional classification algorithm.