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PARAMETER ESTIMATION IN BAYESIAN RECONSTRUCTION OF MULTISPECTRAL IMAGES USING SUPER RESOLUTION TECHNIQUES
| Content Provider | CiteSeerX |
|---|---|
| Author | Rafael Molina, A. Miguel Vega, B. Javier Mateos, A. Aggelos K. Katsaggelos, C. |
| Abstract | In this paper we present a new super resolution Bayesian method for pansharpening of multispectral images which: a) incorporates prior knowledge on the expected characteristics of the multispectral images, b) uses the sensor characteristics to model the observation process of both panchromatic and multispectral images, and c) performs the estimation of all the unknown parameters in the model. Using real data, the pansharpened multispectral images are compared with the images obtained by other parsharpening methods and their quality is assessed both qualitatively and quantitatively. Index Terms — Hierarchical Bayesian modeling, super resolution, image reconstruction, pansharpening multispectral images 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Multispectral Image Expected Characteristic Sensor Characteristic Image Reconstruction Pansharpened Multispectral Image New Super Resolution Bayesian Method Observation Process Prior Knowledge Super Resolution Unknown Parameter Index Term Hierarchical Bayesian Modeling Real Data |
| Content Type | Text |
| Resource Type | Article |