Fast multispectral pansharpening based on a hyper-ellipsoidal color space

In this paper, we present a modified version of a popular component-substitution (CS) pansharpening method, namely the hyperspherical color space (HCS) fusion technique. Unlike other improvements of HCS, the proposed method is insensitive to the format of the data, either calibrated spectral radiance values or uncalibrated digital numbers (DNs), thanks to the use of a multivariate linear regression between the squares of the interpolated MS bands and the squared lowpass filtered Pan, in order to find out the intensity component peculiar of CS methods. The regression of squared MS, instead of the Euclidean radius used by HCS, makes the color space hyper-ellipsoidal instead of hyper-spherical and the intensity component more similar to the lowpass-filtered Pan, such that the extracted detail, namely Pan minus intensity, is more accurate. Furthermore, before the regression is calculated, the interpolated MS bands are diminished by their minima, in order to build a multiplicative injection model with approximately de-hazed components, thereby benefiting from the haze correction, as for all methods exploiting the multiplicative model. Experiments on true GeoEye-1 images show consistent advantages over the baseline HCS and its improvements achieved over time, and a performance comparable with some of the most advanced methods.