Design of Filter Banks for Data Fusion by Spectrum Substitution

In this work, we investigate on the design of filter banks that allow to substitute part of the spectrum of one signal with that of another signal. One application of this technique is fusion of data collected by sensors having different resolutions, that is a a typical problem encountered in remote sensing, when data from low-resolution multi-spectral sensors and high-resolution panchromatic sensors are to be merged, either to alleviate visual identification tasks, or to expedite automatic detection and recognition. The approach that is proposed here is based on the use of cosine-modulated uniform filter banks. We assume that the ratio of the sampling periods of the input data is not integer and show how to design the filter banks so that spectra from different signals can be integrated with minimum distortion.