The hyperspectral image superresolution (HSI-SR) problem aims to improve the spatial
quality of a low spatial resolution HSI by fusing the LR-HSI with the corresponding high …

This paper proposes a method for designing diagonal preconditioners for a preconditioned
primal-dual splitting method (P-PDS), an efficient algorithm that solves nonsmooth convex …

Sparse unmixing is an important technique for analyzing and processing hyperspectral
images (HSIs). Simultaneously exploiting spatial correlation and sparsity improves …

Hyperspectral unmixing is an essential step for the application of hyperspectral images
(HSIs), which estimates endmembers and their corresponding abundances. In recent …

A hyperspectral image sequence can be obtained at different time in the same region from a
hyperspectral sensor. The environmental change usually leads to variation in endmember …

Sparse hyperspectral unmixing has been a hot topic in recent years. Joint sparsity assumes
that each pixel in a small neighborhood of hyperspectral images (HSIs) is composed of the …

Estimating endmembers and corresponding abundances from mixed pixels are essential
steps for hyperspectral unmixing. In hyperspectral unmixing, obtaining accurate unmixing …

Hyperspectral unmixing (HU), an essential procedure for various environmental
applications, has garnered significant attention within remote sensing communities. Among …

Hyperspectral image unmixing gives increasing focus on the structural integrity of the spatial-
spectral information. A large number of methods exploit the sparse and low-rank properties …

Hyperspectral unmixing (HU) is regarded as an indispensable preprocessing procedure for
many field of spectral data analysis because of the existence of mixed pixels. However, the …