Anomaly detection is one of the most important applications in hyperspectral imagery. Real-time processing is the main issue we are facing due to the large data set. Real time causal processing algorithms were developed to perform anomaly detection. It is an innovational kalman filtering based processing by using Woodburys identity to update information which provides the pixel currently being processed without re-processing previous pixels. Experimental results demonstrated the proposed algorithm significantly improves processing efficiency in comparison with conventional anomaly detection without real time causal processing.