An Improved Variational Method for Hyperspectral Image Pansharpening With the Constraint of Spectral Difference Minimization

Abstract

ABSTRACT:Variational pansharpening can enhance the spatial resolution of a hyperspectral (HS) image using a high-resolution panchromatic (PAN) image. However, this technology may lead to spectral distortion that obviously affect the accuracy of data analysis. In this article, we propose an improved variational method for HS image pansharpening with the constraint of spectral difference minimization. We extend the energy function of the classic variational pansharpening method by adding a new spectral fidelity term. This fidelity term is designed following the definition of spectral angle mapper, which means that for every pixel, the spectral difference value of any two bands in the HS image is in equal proportion to that of the two corresponding bands in the pansharpened image. Gradient descent method is adopted to find the optimal solution of the modified energy function, and the pansharpened image can be reconstructed. Experimental results demonstrate that the constraint of spectral difference minimization is able to preserve the original spectral information well in HS images, and reduce the spectral distortion effectively. Compared to original variational method, our method performs better in both visual and quantitative evaluation, and achieves a good trade-off between spatial and spectral information.

Variational Pansharpening for Hyperspectral Imagery Constrained by Spectral Shape and Gram–Schmidt Transformation

“…We propose some improvements on our previous conference paper [28] in this study. The two main improvements of the proposed method are the spectral constraint combined with neighboring pixels and the new correlation constraint.…”

“…Due to the consideration of spatial resolution enhancement, the spectral fidelity term in the proposed method utilizes the spectral information of neighboring pixels and a weight distribution strategy to further decrease the spectral distortion. The method in our previous work [28] directly correct the spectral information according to the HS image, which may lead to large error in edge pixels. The other improvement in this study is designing a new correlation fidelity term.…”

“…The present paper is extended from our previous work [28], designing a variational pansharpening method for HS imagery constrained by spectral shape feature and image correlation. This work focuses on the spectral distortion caused by spatial resolution enhancement and the low correlation between the pansharpened and the actual observed images.…”

Panchromatic and Hyperspectral Image Fusion: Outcome of the 2022 WHISPERS Hyperspectral Pansharpening Challenge

“…An interesting study on the pansharpening performance on HS/Pan data acquired by the same platform or by different platforms was presented in [2]. New classical methods were then proposed, based on the guided filter [9], variational approaches [10], and a component substitution technique improved by saliency analysis [11]. Recently, the number of research papers on HS pansharpening has grown dramatically, in particular related to deep-learning [12]- [24].…”

Hyperspectral Pansharpening in the Reflective Domain with a Second Panchromatic Channel in the SWIR II Spectral Domain

“…• Bayesian and Matrix Factorization (MF) approaches provide high spatial and spectral performance but need prior knowledge about the degradation model, and they imply a higher computation cost [8]. Efficient methods for HS pansharpening are Bayesian Sparse [33,34], a two-step generalisation of Hyperspectral Superresolution (HySure) [35], and a recent variational approach called Spectral Difference Minimization (SDM) [36] for Bayesian approaches as well as Coupled Nonnegative MF (CNMF) [37] and Joint-Criterion Nonnegative MF (JCNMF) [38] for MF approaches.…”