Recently, graph learning methods have attracted much research attention, which uses first-order
nearest-neighbor relation between pixels to construct adjacency graphs for capturing …

Nonnegative matrix factorization (NMF) is widely used in unmixing issue in recent years,
because it can simultaneously estimate the endmembers and abundances. However, most …

Multispectral and hyperspectral image fusion (MHF) aims to reconstruct high-resolution
hyperspectral images by fusing spatial and spectral information. Unlike the traditional canonical …

Hyperspectral remote sensing images have received extensive attention because of their
high spectral resolution. However, the limitation of spatial resolution of imaging spectrometers …

Graph neural networks (GNNs) have outstanding performance in modeling global spatial
information dependence, which makes them very suitable for the complex and diverse …

The problem of multispectral and hyperspectral image fusion (MHF) is to reconstruct images
by fusing the spatial information of multispectral images and the spectral information of …

The purpose of hyperspectral unmixing (HU) is to extract the spectral signatures and their
proportion fractions from the hyperspectral remote sensing image (HSIs), which is a crucial …

This paper introduces a novel hyperspectral image super-resolution algorithm based on
graph-regularized tensor ring decomposition aimed at resolving the challenges of hyperspectral …

Endmember extraction algorithms (EEAs) are among the most commonly discussed types of
hyperspectral image processing in the past three decades. This article proposes a spatial …

There is a growing body of literature on factor analysis that can capture individual and shared
structures in multi-modal data. However, few of these approaches incorporate biological …