Hyperspectral image classification is an important research area in remote sensing data processing，and extensive research has been carried out to obtain higher classification accuracy. The traditional hyperspectral image classification techniques usually concentrate on the information drawn from the spectral domain，while the information of spatial domain is ignored. In this paper，a hyperspectral classification method based on the combination of spectral and spatial information is proposed. Spatial domain methods，such as the region growing method and the binary morphology method，are applied to complement the classification result from the spectral domain information. Experimental results based on a hyperspectral data set show that the proposed method has the capability to increase the classification accuracy.