Spatial Functional Data Analysis for the Spatial-Spectral Classification of Hyperspectral Imagery

Although support vector classifiers for hyperspectral imagery traditionally exploit spectral information alone, there has been increasing interest in spatial-spectral classifiers that incorporate spatial context due to the potential for significant performance improvement over spectral-only approaches. Accordingly, a new approach for spatial-spectral classification is introduced which incorporates spatial information into a prior hyperspectral classifier 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 classification. 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 classifier yields results superior to other state-of-the-art spatial-spectral techniques for hyperspectral classification.