A feature-based image registration algorithm using improved chain-code representation combined with invariant moments

A new feature-based approach to automated image-to-image registration is presented. The characteristic of this approach is that it combines an invariant-moment shape descriptor with improved chain-code matching to establish correspondences between the potentially matched regions detected from the two images. It is robust in that it overcomes the difficulties of control-point correspondence by matching the images both in the feature space, using the principle of minimum distance classifier (based on the combined criteria), and sequentially in the image space, using the rule of root mean-square error (RMSE). In image segmentation, the performance of the Laplacian of Gaussian operators is improved by introducing a new algorithm called thin and robust zero crossing. After the detected edge points are refined and sorted, regions are defined. Region correspondences are then performed by an image-matching algorithm developed in this research. The centers of gravity are then extracted from the matched regions and are used as control points. Transformation parameters are estimated based on the final matched control-point pairs. The algorithm proposed is automated, robust, and of significant value in an operational context. Experimental results using multitemporal Landsat TM imagery are presented.