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

Abstract

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.