A survey of pansharpening methods with a new band-decoupled variational model

Abstract

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.

Target-Adaptive CNN-Based Pansharpening

“…There are in fact many examples of methods which are better cast as statistical [20], [21], [22], [23], [24], [25]. or variational [26], [27] that get state of the art results. However this CS-MRA dichotomy is useful to understand the behaviour of any method falling in these categories as highlighted in [28], [29].…”

Restoration of Pansharpened Images by Conditional Filtering in the PCA Domain

“…The first principal component (PC), having the largest variance, is supposed to contain the structural information. Figure 2 displays the PCs obtained after applying the PCA transform to the pansharpened images by band dependent spatial detail (BDSD) [11], generalized Laplacian pyramid with MTF matched filter and high-pass modulation (GLP) [1] and band-decoupled nonlocal variational model (NLVD) [10]. The PAN and MS data (blue, green, red and near infra-red) were generated following the procedure in Section 3.1.…”

Comparison of Image Fusion Techniques Using Satellite Pour l’Observation de la Terre (SPOT) 6 Satellite Imagery

“…A new generation of pansharpening methods based on deep learning has been evolving in recent years. Component substitution methods rely on the application of a color decorrelation transform to convert unsampled lower-resolution multispectral bands into a new color system that differentiates the spatial and spectral details; fusion occurs by partially or wholly substituting the component that contains the spatial geometry by the panchromatic band and reversing the transformation [23]. Most studies report that while component substitution methods produce pansharpened products of good spatial quality the products suffer spectral distortions.…”

“…Most studies report that while component substitution methods produce pansharpened products of good spatial quality the products suffer spectral distortions. Component substitution is considered more computationally efficient and robust in dealing with mismatches between the multispectral and panchromatic bands [10,23,24]. Typical examples of component substitution methods include principal component analysis (PCA) transform, Brovey's band-dependent spatial detail (BDSD), partial replacement adaptive CS (PRACS), Gram-Schmidt (GS) orthonormalization, and intensity-hue-saturation (IHS) transform.…”