Kernel Eigenspace Separation Transform for Subspace Anomaly Detection in Hyperspectral Imagery

Abstract

This letter proposes a nonlinear version of the eigenspace separation transform (EST) for subspace anomaly detection in hyperspectral imaging. The EST is defined in terms of the eigenvectors of the difference correlation matrix (DCOR) obtained using the data from the two classes. Using ideas found in the machine learning literature (i.e., the kernel trick), a nonlinear version-kernel EST (KEST)-is achieved by expressing the DCOR in terms of dot products in feature space and replacing all dot products with a Mercer kernel function that is defined in terms of input data space. Experimental results indicate that KEST outperforms many other commonly used subspace anomaly detection algorithms.