摘要： Although support vector classi?ers for hyperspectral imagery traditionally exploit spectral information alone, there has been increasing interest in spatial–spectral classi?ers that incorporate spatial context due to the potential for signi?cant performance improvement over spectral-only approaches. Accordingly, a new approach for spatial–spectral classi?cation is introduced which incorporates spatial information into a prior hyperspectral classi?er driven by functional data analysis (FDA) applied to continuous spectral functions. FDA permits functional properties—such as the smoothness inherent to spectral signatures—to inform hyperspectral classi?cation. The proposed spatial FDA (SFDA) incorporates an additional spatial coherency factor that attempts to ensure that each pixel is represented with a spectral curve that is similar to those of its nearest spatial neighbors. Experimental results demonstrate that the proposed SFDA coupled with a support vector classi?er yields results superior to other state-of-the-art spatial–spectral techniques for hyperspectral classi?cation.