RX and its variants for anomaly detection in hyperspectral images

Kernel matrix was regularized for improving the stability of the kernel RX algorithm in anomaly detection for hyperspectral images, and regularized kernel RX (rkRX) algorithm was proposed. The RX algorithm was improved by merging the normalized regularized RX algorithm and the regularized kernel RX algorithm, named merging RX algorithm (mRX). Considering the results of both original linear space and high dimensional feature space at the same time, it produced an improved and more stable performance in anomaly detection. Compared with original RX, regularized RX (rRX) and kernel RX (kRX), the above two algorithms used double window technique, kernel principal component analysis (KPCA) feature extraction and feature selection based on high order statistics as a preprocessing for reducing data dimension, in simulation images and the real hyperspectral images experiments. Also, the five algorithms were compared in the images data without dimension reduction. Finally, the ROC curves were painted for evaluating the detection performances. The results show that the proposed two algorithms improve the detection performance and have certain robustness.