Kernel Structural SIMIlarity on hyperspectral images

In this paper, we introduce a non-linear and multidimensional generalization of the Structural SIMilarity index (SSIM) for quality assessment of hyperspectral images. We exploit well-known properties of functional analysis and estimate means, variances, and correlation in proper reproducing kernel Hilbert spaces (rkHs). The so-called Kernel SSIM (KSSIM) is shown to generalize the conventional SSIM and the recently introduced Q₄ and Q_n metrics for remote sensing applications, and naturally works with multidimensional images. For the experimentation, we built a database of different distortions commonly encountered in remote sensing images. KSSIM shows an improved agreement with classification results compared to standard similarity metrics, and high consistency for different noise sources and levels.