Relationship among orthogonal subspace projection, constrained energy minimization and RX-algorithm

Orthogonal Subspace Projection (OSP) has been shown a successful technique for hyperspectral image analysis. It requires a linear mixture model with complete target knowledge to perform sub-pixel detection and mixed classification. Constrained energy minimization (CEM) has been also shown to be effective in sub-pixel detection and mixed pixel classification which only needs the knowledge of targets of interest. RX-algorithm which has been widely used for anomaly detection in signal processing does not require any prior target information. Interestingly, these three techniques are closely related from an aspect of information being used in these three techniques. They all perform some sort of matched filter with different levels of information used in the filter. This paper investigates and explores their relationship that sheds light on their algorithm design.