Abstract
The kernel RX detector (KRXD) has better performance than the RX algorithm in anomaly detection (AD). However, it generally suffers from two challenges: 1) it is more prone to background contamination by anomalous pixels and noise in local statistics since the local AD is normally implemented for KRXD to relieve high computational complexity in global AD and 2) the inverse of the kernelized background covariance matrix is usually rank deficient. Accordingly, this letter proposes a Gaussian background purification approach according to background data samples probability distribution and an inverse-of-matrix-free method based on kernel PCA to address the above problems, respectively. The experimental results indicate that the improved KRXD overcomes both the difficulties and procures preferable effects.
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