Sharpening of very high resolution images with spectral distortion minimization

This work presents a viable solution to the problem of merging a miltispectral image with an arbitrary number of bands with a higher-resolution panchromatic observation. The proposed method relies on the generalized Laplacian pyramid, which is a multiscale oversampled structure in which spatial details are mapped on different scales. The goal is to selectively perform spatial-frequencies spectrum substitution from an image to another with the constraint of thoroughly retaining the spectral information of the coarser data. To this end, a vector injection model has been defined: at each pixel, the detail vector to be added is always parallel to the approximation. Furthermore, its components are scaled by factors measuring the ratio of local gains between the multispectral and panchromatic data. Such a model is calculated at a coarser resolution where both types of data are available and extended to the finer resolution by embedding the modulation transfer functions of the multispectral scanner into the multiresolution analysis. In this way, the interband structure model can be extended to the higher resolution without the drawback of the poor enhancement occurring when the model assumes MTFs close to be ideal. Results are presented and discussed on very high resolution QuickBird data of an urban area.