Constrained Energy Minimization Anomaly Detection for Hyperspectral Imagery via Dummy Variable Trick

CEM has shown great success in subpixel target detection. This article develops a DVT to extend CEM to CEM-AD and shows that CEM-AD also enjoys the same success in anomaly detection (AD). Its idea converts a known specific target signaturedimposed on CEM into an unknown specific target signature to develop an SBR-CEM as a UST-CEM which serves as a liaison to derive the desired CEM-AD without prior knowledge ofd. Surprisingly, the derived CEM-AD turns out to be a sample correlation matrixR-based AD,R-AD in correspondence to the well-known sample covariance matrixK-based AD developed by Reed-Xiaoli, RX-AD. To further improve CEM-AD, an LRaSMD model introduced by GoDec and its SC are further incorporated into CEM-AD where two new versions of SC, FSC and VSC, are particularly designed to enhance AD. Finally, to effectively evaluate AD performance, recently developed 3-D ROC curve-derived detection measures are used for comparative studies and analyses.