<title>Nonlinear mean-square estimation with applications in remote sensing</title>

An approach to image modeling based on nonlinear mean-square estimation that does not assume a functional form for the model is described. The relationship between input and output images is represented in the form of a lookup table that can be efficiently computed from, and applied to images. Three applications are presented to illustrate the utility of the technique in remote sensing. The first illustrates how the method can be used to estimate the values of physical parameters from imagery. Specifically we estimate the topographic component (i.e., the variation in brightness caused by the shape of the surface) from multispectral imagery. The second application is a nonlinear change detection algorithm which predicts one image as a nonlinear function of another. In cases where the frequency of change is large (e.g., due to atmospheric and environmental differences), the algorithm is shown to be superior in performance to linear change detection. In the last application, a technique for removing wavelength- dependent space-varying haze from multispectral imagery is presented. The technique uses the IR bands, which are not affected significantly by haze, to predict the visible bands. Results show a significant reduction in haze over the area considered. Additional application areas are also discussed.