Development of a feature-based approach to automated image registration for multitemporal and multisensor remotely sensed imagery

In this paper, a new feature-based approach to automated multitemporal and multisensor image registration is presented. The characteristics of this technique is that it combines moment invariant shape descriptors with modified chain code correlation to establish the correspondences between potential matched regions in two images. It also overcomes the difficulties in control point correspondence in image matching caused by the problem of feature inconsistency. In image segmentation, we use the improved Laplacian of Gaussian (LoG) zero-crossing edge detector. Feature matching is done in both feature space and image space based on moment invariant distance and improved chain code correlation. The centers of gravity are then extracted from matched regions and used as control points. The final transformation parameters are estimated based on the final matched control points. Experimental results using multitemporal Landsat TM imagery are presented.