Hyperspectral anomaly detection using an optimized support vector data description method

In this paper, an optimal support vector based method to detect anomalies in hyperspectral images is presented. This method is based on a technique called Support Vector Data Description (SVDD) which learns the support of local background distribution by modeling an enclosing hypersphere around this data in a high dimensional feature space associated with Gaussian Radial Basis Function (RBF) kernel. Any test pixel that lies outside this hypersphere surrounding the local background is considered an anomaly and hence, a possible target pixel. For the Gaussian RBF kernel to perform well, the bandwidth parameter of the kernel function needs to be set optimally. The proposed algorithm is a two-step iterative method to optimize for this parameter: the volume of the enclosing hypersphere is minimized by optimizing the support vectors in one step and then subsequently further minimized with respect to the kernel bandwidth parameter in the next. Considerable increase in detection performance due to kernel parameter optimization can be seen in the simulation results when the algorithm is applied to real hyperspectral images.