Application to Environmental Surveillance: Dynamic Image Estimation Fusion and Optimal Remote Sensing with Fuzzy Integral

Image fusion has been increasingly important in environmental surveillance. It is well-known that multi-scale decomposition methods lack of spatial-temporal adaptability. To tackle this problem, based on Kalman filtered compressed sensing, a dynamic image estimation fusion framework is provided. A parametric spatial-temporal fusion method is proposed for modelling fusion pattern. This method is capable of learning model parameters adaptively in state space. Furthermore, an optimal fusion method for remote sensing images is presented, which exploits statistical property of the decomposition coefficients and utilizes the fuzzy integral. The numerical experiments on the ground-truth datasets indicate the efficiency and effectiveness of the proposed methods.

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