Joint Weighted Schatten-p Norm and Spatial Smoothness Regularization for Hyperspectral and Multispectral Image Fusion With Spectral Variability

Han Pan, Zhongliang Jing, Henry Leung, Weizhi Qu

Abstract: Hyperspectral (HS) and multispectral (MS) images’ fusion aims to improve their spatial resolutions and circumvent the main limitation of HS sensors. However, existing HS–MS fusion methods account for spectral variability fail to consider the global spectral correlation. To overcome this problem, this letter presents a novel joint weighted Schatten-p norm and spatial smoothness regularization for HS–MS fusion account for both spatial and spectral changes. First, the relationship between the spectral variability and the spectral signatures is formulated as an explicit parametric model. Second, to preserve the inherent correlation among the bands, we design a weighted Schatten-p ( $ 0\lt p\lt 1 $ ) norm regularization method, which considers the importance of different components. Third, a spatial smoothness regularization term is exploited to reconstruct the spatial details. Finally, an iterative procedure based on the framework of alternating direction method of multipliers (ADMM) is designed to solve the resulting optimization problem. Extensive experiments on both synthetic and real datasets demonstrate that the proposed method outperforms six state-of-the-art methods from visual and quantitative assessments. The datasets and results are released in http://github.com/phan1007/WSGS.

External IDs:dblp:journals/lgrs/PanJLQ24