Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery

作者：Heesung Kwon, Nasser M. Nasrabadi · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2005 · DOI：10.1109/tgrs.2004.841487 · 被引用次数：817 · 研究领域：Remote-Sensing Image Classification、Advanced Chemical Sensor Technologies、Sparse and Compressive Sensing Techniques

We present a nonlinear version of the well-known anomaly detection method referred to as the RX-algorithm. Extending this algorithm to a feature space associated with the original input space via a certain nonlinear mapping function can provide a nonlinear version of the RX-algorithm. This nonlinear RX-algorithm, referred to as the kernel RX-algorithm, is basically intractable mainly due to the high dimensionality of the feature space produced by the nonlinear mapping function. However, in this paper it is shown that the kernel RX-algorithm can easily be implemented by kernelizing the RX-algorithm in the feature space in terms of kernels that implicitly compute dot products in the feature space. Improved performance of the kernel RX-algorithm over the conventional RX-algorithm is shown by testing several hyperspectral imagery for military target and mine detection.