Multiple kinds of features extracted from hyperspectral imagery (HSI) have shown great potential for pixel-oriented classification. However, two difficulties can be encountered during the classification process. Firstly, it is time consuming to directly utilize the large amount of features. Secondly, because each kind of feature is usually processed individually, the high-level relationship among different features is not completely configured, decreasing the performance eventually. In this paper, a new strategy to fuse the features and exploit dictionary learning for HSI classification is proposed. Based on the high-level relationship, the extracted Gabor features have been integrated into a more compact and more discriminative representation through a Fisher-based criterion. Experimental results have shown that the fused features can not only produce competitive performance for HSI classification, but also greatly reduce the computational complexity.