Improving Component Substitution Pansharpening Through Multivariate Regression of MS $+$Pan Data

TLDR

The study proposes using multivariate regression to enhance spectral fidelity in component‑substitution pansharpening while preserving spatial detail. A general framework is introduced that models any CS fusion method by defining a generalized intensity component as a weighted average of multispectral bands, where the weights are regression coefficients derived from the relationship between the bands and a spatially degraded panchromatic image. When applied to Gram‑Schmidt and generalized intensity‑hue‑saturation fusion, the preprocessing module produces images with unchanged spatial sharpness but improved spectral quality, as confirmed by quantitative metrics that outperform baseline methods.

Abstract

In this paper, multivariate regression is adopted to improve spectral quality, without diminishing spatial quality, in image fusion methods based on the well-established component substitution (CS) approach. A general scheme that is capable of modeling any CS image fusion method is presented and discussed. According to this scheme, a generalized intensity component is defined as the weighted average of the multispectral (MS) bands. The weights are obtained as regression coefficients between the MS bands and the spatially degraded panchromatic (Pan) image, with the aim of capturing the spectral responses of the sensors. Once it has been integrated into the Gram-Schmidt spectral-sharpening method, which is implemented in environment for visualizing images (ENVI) program, and into the generalized intensity-hue-saturation fusion method, the proposed preprocessing module allows the production of fused images of the same spatial sharpness but of increased spectral quality with respect to the standard implementations. In addition, quantitative scores carried out on spatially degraded data clearly confirm the superiority of the enhanced methods over their baselines.