High-performance fusion of multispectral and hyperspectral data

Hyperspectral imagers tend to have lower spatial resolution than multispectral ones. This often results in a (sometimes difficult) trade-off between spectral and spatial resolution. One means of addressing this spatial/spectral resolution trade-off is to acquire both multispectral and hyperspectral data simultaneously, and then combine the two to produce a hyperspectral image with the high spatial resolution of the multispectral image. This process, called 'sharpening', results in a product that fuses the rich spectral content of a hyperspectral image with the high spatial content of the multispectral image. The approach we have been investigating compares the spectral information present in the multispectral image to the spectral content in the hyperspectral image and derives a set of equations to approximately transform the multispectral image into a synthetic hyperspectral image. This synthetic hyperspectral image is then recombined with the original low-spatial-resolution hyperspectral image to produce a sharpened product. We have evaluated this technique against several types of data for terrain classification and it has demonstrated good performance across all data sets. The spectra predicted by the sharpening algorithm match truth spectra in synthetic image tests, and performance with detection algorithms show little, if any, degradation of detection performance.