5 February 2004 Joint deconvolution and interpolation of remote sensing data
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Abstract
We present a method for the simultaneous deconvolution and interpolation of remote sensing data in a single joint inverse problem. Joint inversion allows sparsely sampled data to improve deconvolution results and, conversely, allows large-scale blurred data to improve the interpolation of sampled data. Geostatistical interpolation and geostatistically damped deconvolution are special cases such a joint inverse problem. Our method is posed in the Bayesian framework and requires the definition of likelihood functions for each data set involved, as well as a prior model of the parameter field of interest. The solution of such a problem is the posterior probability distribution. We present an algorithm for finding the maximum of this distribution. The particular application we apply our algorithm to is the fusion of digital elevation model and global positioning system data sets. The former data is a larger scale blurred image of topography, while the latter represent point samples of the same field. A synthetic data set is constructed to first show the performance of the method. Real data is then inverted.
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Jonathan A. Kane, William Rodi, "Joint deconvolution and interpolation of remote sensing data", Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); doi: 10.1117/12.511354; https://doi.org/10.1117/12.511354
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