Kernel and Stochastic Expectation Maximization: A Fusion Algorithm for Target Detection in Hyperspectral Imagery

In this paper, we present a new algorithm for target detection using hyperspectral imagery. The proposed algorithm is inspired by the outstanding performance of nonlinear Kernel method and the robustness of the stochastic expectation maximization (SEM) algorithm. The traditional technique of using SEM algorithm for target detection in hyperspectral imagery is associated with dimensionality reduction of the input data using binning or principal components analysis (PCA) algorithm. Although, the data reduction of the input data is enforced to reduce the computational burden on SEM algorithm, but it affects the results of target detection, especially the challenging one, due to not using the entire information of the potential targets. To facilitate detection of the target by using the entire targets information and simultaneously reducing the computational burden on SEM algorithm, we propose a new scheme for data reduction based on using Kernels. Kernel-based input data reduction is a nonlinear filtering technique in which the input data are mapped to the feature space where most of the background data is filtered using an easily selected threshold. Then, Gaussian mixture model is generated for the reduced input-data and SEM algorithm is employed to estimate the model parameters and to classify that input data. Finally, we allocated the target’s class and isolated the target pixels. The proposed scheme for fusion the kernel with SEM algorithm has been tested using real life hyperspectral imagery and the results show superior performance compared to alternate algorithms.