Multispectral image data fusion under a Bayesian approach

Multispectral image data fusion is understood here as a process that generates synthetic images from a combination of primary images, by attempting to preserve the best characteristics of each primary image. The obtained product is important in helping the users of remote sensing in visual analysis. This paper describes a new method for multispectral image data fusion using a Bayesian framework. As an example, the method is applied to the synthesis of new bands for the SPOT satellite. The method comprises two steps: (a) a new interpolator for the multispectral bands, obtained through the orthogonality principle and leading to the estimator and its covariance.matrix, which will be used as a priori information for the second step; and (b) a new statistical synthesis formulation, also obtained through the orthogonality principle, using as observations the panchromatic and the multispectral bands. Experimental results with SPOT images over Guarulhos Airport, São Paulo, Brazil, are presented, including both the interpolated multispectral bands and the synthetic bands. A discussion of the visual output also includes a comparison between the new interpolation process with conventional interpolators such as nearest neighbour and bilinear. Furthermore, the flexibility of the Bayesian interpolator is obtained through the possibility of using different horizontal and vertical correlation coefficients that adapt the model to the local characteristics of the image being interpolated. The proposed interpolation method also allows the use of a simple unsharp masking procedure, with improved visual edge delineation.