A Survey of Pansharpening Methods with A New Band-Decoupled Variational Model
This addresses the problem of improving spatial resolution while preserving spectral information in satellite imagery for remote sensing applications, representing a novel method for a known bottleneck.
The paper tackled pansharpening by proposing a new band-decoupled variational model that minimizes a cost function with nonlocal regularization, dropping the linear transformation assumption for a radiometric ratio constraint. The method outperformed classical and state-of-the-art techniques in preserving spatial details, reducing color distortions, and avoiding aliasing artifacts, as shown in an exhaustive performance comparison.
Most satellites decouple the acquisition of a panchromatic image at high spatial resolution from the acquisition of a multispectral image at lower spatial resolution. Pansharpening is a fusion technique used to increase the spatial resolution of the multispectral data while simultaneously preserving its spectral information. In this paper, we consider pansharpening as an optimization problem minimizing a cost function with a nonlocal regularization term. The energy functional which is to be minimized decouples for each band, thus permitting the application to misregistered spectral components. This requirement is achieved by dropping the, commonly used, assumption that relates the spectral and panchromatic modalities by a linear transformation. Instead, a new constraint that preserves the radiometric ratio between the panchromatic and each spectral component is introduced. An exhaustive performance comparison of the proposed fusion method with several classical and state-of-the-art pansharpening techniques illustrates its superiority in preserving spatial details, reducing color distortions, and avoiding the creation of aliasing artifacts.