Adaptive causal anomaly detection for hyperspectral imagery

Anomaly detection finds target pixels whose signatures are spectrally distinct from their surrounding pixels. It is generally performed without prior knowledge. This paper presents an adaptive causal anomaly detector (ACAD) which implements a causal anomaly detector in such a fashion that a target pixel will be removed from the data correlation matrix once it is detected as an anomaly. As a result, it improves the commonly used RX algorithm as well as a recently developed causal RX filter