ADAPTIVE ALGORITHMS FOR CHANGE DETECTION IN IMAGE SEQUENCE

In this paper we present two adaptive algorithms for detection of small changes/targets (of the order of one pixel) in a pair of images in a low signal to clutter plus noise ratio (SCNR) environment (SCNR is of the order of -14.5 dB). They both have the ability to track the non-stationary image signals and suppress the clutter plus noise background. Both detectors are based on adaptive correlation canceling technique. The first one uses an order recursive least squares (ORLS) lattice filter, while the second is based on the two-dimensional least square (TDLMS) algorithm. The only a priori information required is that the background clutter plus noise in the pair of images is spatially correlated. An analytical expression for the improvement factor of the suggested change detectors is presented. Also, the influence of the order of the ORLS lattice filter and of the algorithm parameters of the TDLMS on their detection performances is studied. The performances of the two algorithms are evaluated by using an optical satellite image with computer generated target and noise added to it.