期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2002
卷号：XXXIV Part 3 A
页码：394-399
出版社：Copernicus Publications
摘要：One of the main obstacles in image registration is the precise estimation of a mapping function that determines geometric transformation between two image coordinate systems. For conventional image registration methods, their registration results are not the global optimal, and accuracy is low because only a few local control points are used for the estimation. In this paper, we develop a global optimal method in order to get a registration approach with high accuracy. In our method, an energy function that is directly related to the parameters of the mapping function is defined in the whole image. Thus, estimation of the global optimal mapping function can be solved through energy optimization. In defining the energy function, we choose a strength measure that is based on contour edge points. It is demonstrated that the strength measure is insensitive to image radiometric distortion. Therefore, our method is applicable for various kinds of images, even for different sensors images. In order to solve the energy optimization, we design a pipelining hybrid framework that combines genetic algorithms (GAs) and a simplex method (SM). The GAs are applied firstly to look for a few initial guesses from some sub-images, and then the SM is employed to get the optima of the energy function near these initial guesses. It is found that the pipelining hybrid framework is not trapped in a local optimum, and converges fast. Hence, one of the advantages of our algorithm is that it successfully avoids advanced feature extraction and feature matching in the image registration. Its characteristics are of automatic and robust. Experimental results have shown that our method can provide better accuracy than the manual registration
