Kernel canonical correlation analysis for hyperspectral anomaly detection

Abstract

In this paper, we present a kernel-based nonlinear version of canonical correlation analysis (CCA), so called kernel canonical correlation analysis (KCCA), for hyperspectral anomaly detection applications. CCA only measures linear dependency between two sets of signal vectors (target and background) ignoring higher order correlations crucial for distinguishing between man-made objects and background clutter. In order to exploit nonlinear correlations we implicitly map the two sets of data into a high dimensional feature space where correlations of nonlinear features extracted from the original data are exploited by a kernel function. A generalized eigenproblem is then formulated for KCCA. In this paper, both CCA and KCCA are applied to real hyperspectral images and detection performance of CCA and KCCA are compared to the well-known RX anomaly detection algorithm.