Design and Analysis of Hyperspectral Anomaly Detection Based on Kalman Filter Theory

Anomaly detection generally gains wide attention in hyperspectral imagery for its high spectral resolution. Real-time processing is badly needed due to its large data set. This paper presents real-time processing versions to implement the commonly used RX anomaly detector which make use of information only provided by previous pixels prior to currently being processed pixel. Through these algorithms, hyperspectral image data can be processed timely. Experimental results demonstrate these new real-time versions significantly solve real-time processing problem compared to conventional anomaly detector.