Adaptive change detection in image sequence

Two adaptive algorithms are presented for the detection of small changes in a pair of images in a low signal to clutter plus noise ratio (SCNR) environment. They both have the ability to track the nonstationary image signals and suppress the clutter plus noise background. Both detectors are based on the adaptive correlation canceling technique. One algorithm uses an order recursive least squares (ORLS) lattice filter, while the other is based on the two-dimensional least mean square (TDLMS) algorithm. The only a priori information required by the algorithms is that the background clutter plus noise in the pair of images is spatially correlated. An analytical expression for the improvement factor for the change detectors is presented. The performance of the two algorithms is evaluated by using an optical satellite image, with computer generated target and noise added