A RX-based Hyperspectral Target Detection Method By Fusing Two Kernels

The paper discusses a kernel RX algorithm for hyperspectral target detection. Because it is difficult to estimate the covariance matrix accurately for background areas, directly using the RX Algorithm for hyperspectral target detection is not a good choice in many cases. Therefore, we apply a kernel RX algorithm to our application. The kernel RX algorithm has good nonlinear anomaly detection ability due to its nonlinear mapping from the low dimensional data space to a high dimensional feature space. On the basis of a Gaussian kernel function, we propose a hybrid kernel RX (H-KRX) algorithm by adding a modified spectral angle kernel function to the original Gaussian kernel. Experiments are put into effect based on our tested hyperspectral data and the public AVIRIS 92AV3 data sets. The results indicate that the proposed method can improve the hyperspectral target detection accuracy by 5% with a similar false alarm rate.