Multiple-sensor processing is considered, and a unified method for representing multiple-sensor data is developed. When resolution varies between sensors, such a multiple-sensor system can be viewed as samples of a scale-space signal representation. It is demonstrated that if the spatial transfer function of the sensors are Gaussian, then scale-space filtering can be used to recover small-scale (fine-resolution) information through extrapolation in scale. As an example of multiple-sensor processing, multispectral processing of remote sensing, in which images of surface scenes are simultaneously generated at different (center) frequencies, is considered. The fingerprints of extrapolated signals approximate the actual multispectral fingerprints at small scales and can be used when the multispectral fingerprints are not available. >