An anisotropic spatial modeling approach for remote sensing image rectification

Rectification of a remote sensing image is commonly done by applying polynomial regression models to image coordinates and map coordinates of ground control points. A major drawback of the polynomial regression model is that it does not capture the random characteristic of terrain elevation. In fact, the distortion of a remote sensing image is attributed to the variation of terrain elevation and orbital parameters, the variations being random in nature. A more effective approach of remote sensing image rectification is a stochastic approach that takes into account the spatial variation structure of terrain elevation. This article presents an anisotropic spatial modeling approach of image rectification using ordinary kriging estimation. By considering the residuals of polynomial trend mapping as anisotropic random fields, the proposed approach models separately the spatial variation structures of the residuals in X and Y directions, and employs the ordinary kriging method for spatial interpolation of the residual random fields. By means of a cross validation procedure, residuals of image rectification by the polynomial trend mapping, the multiquadric interpolation function, and the ordinary kriging approaches are compared. The ordinary kriging approach yields smallest variances and root-mean-squared of mapping errors. (C) Elsevier Science Inc., 2000.