A Fast Recursive Collaboration Representation Anomaly Detector for Hyperspectral Image

Even though collaboration representation-based detector (CRD) performs well for hyperspectral image (HSI) anomaly detection, its computational cost is too high for the widely demanded real-time applications. To reduce the computational complexity, a recursive CRD is proposed in this letter. By constructing two elementary transformation matrices in accordance with the location of the pixels, a recursive update approach is derived by a matrix inversion lemma to speed up the detector. Experimental results on two real HSI data sets show that the proposed method saves over 30% processing time without accuracy loss.