Whitening spatial correlation filtering for hyperspectral anomaly detection

Matched and adaptive subspace detectors apply to a wide range of problems in radar, sonar, and data communication, where the signal is constrained to lie in a multidimensional linear subspace. These detectors generalize known results in matched and adaptive detection theory. In this paper we propose an original approach to anomaly detection based on whitening and spatial correlation filtering (WSCF). The performance is investigated in terms of the detection probability, and the false alarm ratio. A comparison permits us to show how this new method can outperform the well-known Reed and Xiaoli Yu (RX) algorithm.