Reducing Computational Complexity in Hyperspectral Anomaly Detection: a Feature Level Fusion Approach

In this paper a new strategy aimed at reducing the computational complexity in hyperspectral anomaly detection is introduced. It is based on the fusion of the results obtained by applying the RX detector to the data measured by the different optical systems in the adopted hyperspectral sensor. Two feature level fusion criteria are derived and the computational complexity of each of them is evaluated. A comparison among the RX algorithm detection performance and the ones of the proposed anomaly detectors is provided by considering a data set acquired by an airborne hyperspectral sensor.