Cascaded Spectral–Spatial Hyperspectral Anomaly Detection

An Iterative spectral-spatial hyperspectral anomaly detection (ISSHAD) was recently developed to augment the data cube to be processed to construct a new data cube via a feedback process in an iterative manner. As a result of such a feedback process hyperspectral anomaly detection (ISSHAD) significantly improve HAD performance. As an opposite approach to ISSHAD, this paper develops a new approach to HAD, called cascaded spectral-spatial hyperspectral anomaly detection (CSSHAD), which performs HAD by a cascade of anomaly detection modules (ADMs) in a feedforward network in which each cascaded anomaly detection module (ADM) implements the same anomaly detector to produce an anomaly detection map (ADMap). Then a spatial filter is designed to capture spatial information from the ADMap to generate a real-valued spatial filtered ADMap (SF-Map) which will be used as the input to the next following ADM. Finally, the depth of CSSHAD is determined by an automatic stopping rule. To further improve anomaly detectability and background suppressibility, a recent developed concept, effective anomaly space (EAS) is incorporated into CSSHAD to derive EAS-CSSHAD. Unlike ISSHAD which is a feedback HAD network, CSSHAD is a feedforward HAD network that operates a cascade of ADMs by feeding forward SFMaps in a similar manner that ISSHAD uses an iterative feedback process. Accordingly, CSSHAD and ISSHAD can be considered to dual HAD methods as companion methods. Experimental results also demonstrate that CSSHAD performs comparably to ISSHAD with less computational complexity due to its use of feedforward structure rather than feedback process.