Rare signal component extraction based on kernel methods for anomaly detection in hyperspectral imagery

Abstract

Anomaly detection is one of hot research topics in hyperspectral remote sensing. For this task, RX detector (RXD) is a benchmark method. Unfortunately, Gaussian distribution assumption adopted by RXD cannot be well satisfied in hyperspectral images due to high dimensionality of data and complicated correlation between spectral bands. In this paper, we address this problem and propose an algorithm called rare signal component extraction (RSCE), aiming at finding a subspace where the Gaussian assumption is well obeyed and improving detection performance of RXD. RSCE algorithm first utilizes kernel singular value decomposition (KSVD) to construct a kernel-based whitening operator, and then, carries out kernel-based whitening on hyperspectral data. After that, RSCE algorithm is to extract and determine a singular signal subspace by means of independent component analysis in reproducing kernel Hilbert space (RKHS) space and singularity measure. Numerical experiments were conducted on two real hyperspectral datasets. The experimental results show that the proposed RSCE algorithm greatly improves the detection performance of RXD and outperforms other state-of-the-art methods.