Pan-Sharpening of Very High-Resolution Multispectral Images via Generalised Laplacian Pyramid Fusion

This work presents a viable solution to the problem of fusion of multispectral (MS) images with high-resolution panchromatic (P) observations. The method relies on the generalized Laplacian pyramid (GLP), which is obtained by recursively subtracting from an image its low-pass version, and works even with fractional scale ratios between the data to be merged. Unlike other multiscale fusion schemes, the decomposition proposed for the multiresolution analysis underlying the fusion procedure is not critically sub-sampled, thus avoiding possible inaccuracies and impairments in the fused images, originated from the missing cancellation of aliasing terms. The fusion method selectively performs spatial-frequencies spectrum substitution from the higher-resolution (P) image to the coarser (MS) bands. Several detail injection models, both global and local, are presented. Spectral distortion of the fused product is addressed in devising an injection model that is capable to constrain to zero the spectral angle between resampled MS bands, taken as reference of spectral fidelity, and fused product at every pixel. Results are presented and discussed on very high-resolution SPOT 5 data of an urban area, simulated by means of hyperspectral data collected by the MIVIS air-borne spectrometer.