Hyperspectral remote sensing sensors can capture hundreds of contiguous spectral images and provide plenty of valuable information. Feature selection and classification play a key role in the field of HyperSpectral Image (HSI) analysis. This paper addresses the problem of HSI classification from the following three aspects. First, we present a novel criterion by standard deviation, Kullback-Leibler distance, and correlation coefficient for feature selection. Second, we optimize the SVM classifier design by searching for the most appropriate value of the parameters using particle swarm optimization (PSO) with mutation mechanism. Finally, we propose an ensemble learning framework, which applies the boosting technique to learn multiple kernel classifiers for classification problems. Experiments are conducted on benchmark HSI classification data sets. The evaluation results show that the proposed approach can achieve better accuracy and efficiency than state-of-the-art methods.