Bayesian Data Fusion: Spatial and Temporal Applications

Because the characteristics of remotely sensed data vary greatly with the sensors, spectral and spatial resolutions are practically unique for each sensor. Therefore, there is a real need for a theoretical framework that aims at merging information from two or more different sources. In this paper, a new Bayesian data fusion (BDF) framework is used in order to tackle several classical remote sensing issues. This BDF framework is dedicated to spatial prediction, which draws new avenues for applications in remote sensing. An existing BDF method proposed for the pansharpening of IKONOS image is adapted in the case of SPOT 5 image. The BDF approach is then tested for the enhancement of the spatial resolution of coarse images with high-resolution images. In order to illustrate these methods, SPOT 5 and SPOT VEGETATION images were purchased at two different dates in die province of Ninh Thuan (Vietnam). Finally, prospective considerations are addressed for updating past high-resolution images with recent coarse images.