Comparative analysis of hyperspectral anomaly detection strategies on a new high spatial and spectral resolution data set

This work presents a comparative experimental analysis of different Anomaly Detectors (ADs) carried out on a high spatial resolution data set acquired by the prototype hyperspectral sensor SIM-GA. The benchmark AD for hyperspectral anomaly detection is the Reed-Xiaoli (RX) algorithm. Its main limitation is the assumption that the local background can be modeled by a Gaussian distribution. In the literature, several ADs have been presented, most of them trying to cope with the problem of non-Gaussian background. Despite the variety of works carried out on such algorithms, it is difficult to find a comparative analysis of these methodologies performed on the same data set and therefore in identical operating conditions. In this work, the most known ADs, such as the RX, Orthogonal Subspace Projection (OSP) based algorithms, the Cluster Based AD (CBAD), and the Signal Subspace Processing AD (SSPAD) are analyzed and compared, highlighting their most interesting characteristics. The performance is evaluated on a new data set relative to a rural scenario, in which several man-made targets have been embedded. The non-homogeneous nature of the background, enhanced by the high spatial resolution of the sensor, and the presence of man-made artifacts, like buildings and vehicles, make the anomaly detection process very challenging. Performance comparison is carried out on the basis of a joint analysis of the Receiving Operative Characteristics and the image statistics. For this data set, the best performance is obtained by the strong background suppression ability of the OSP-based algorithm.