Kernel-based Constrained Energy Minimization (K-CEM)

Kernel-based approaches have recently drawn considerable interests in hyperspectral image analysis due to its ability in expanding features to a higher dimensional space via a nonlinear mapping function. Many well-known detection and classification techniques such as Orthogonal Subspace Projection (OSP), RX algorithm, linear discriminant analysis, Principal Components Analysis (PCA), Independent Component Analysis (ICA), have been extended to the corresponding kernel versions. Interestingly, a target detection method, called Constrained Energy Minimization (CEM) which has been also widely used in hyperspectral target detection has not been extended to its kernel version. This paper investigates a kernel-based CEM, called Kernel CEM (K-CEM) which employs various kernels to expand the original data space to a higher dimensional feature space that CEM can be operated on. Experiments are conducted to perform a comparative analysis and study between CEM and K-CEM. The results do not show K-CEM provided significant improvement over CEM in detecting hyperspectral targets but does show significant improvement in detecting targets in multispectral imagery which provides limited spectral information for the CEM to work well.