Modern hyperspectral imaging systems produce huge datasets potentially conveying a great
abundance of information; such a resource, however, poses many challenges in the …

Hyperspectral imaging (HSI) is a powerful tool that can capture and analyze a range of
spectral bands, providing unparalleled levels of precision and accuracy in data analysis …

Over the past decades, enormous efforts have been made to improve the performance of
linear or nonlinear mixing models for hyperspectral unmixing (HU), yet their ability to …

In recent years, deep learning (DL) has attracted increasing attention in hyperspectral
unmixing (HU) applications due to its powerful learning and data fitting ability. The …

Blind hyperspectral unmixing is the process of expressing the measured spectrum of a pixel
as a combination of a set of spectral signatures called endmembers and simultaneously …

Hyperspectral unmixing has been an important technique that estimates a set of
endmembers and their corresponding abundances from a hyperspectral image (HSI) …

Deep learning (DL) has been widely applied to hyperspectral image (HSI) classification,
owing to its promising feature learning and representation capabilities. However, limited by …

Deep learning (DL) has heavily impacted the data-intensive field of remote sensing.
Autoencoders are a type of DL methods that have been found to be powerful for blind …

Spectral unmixing is central when analyzing hyperspectral data. To accomplish this task,
physics-based methods have become popular because, with their explicit mixing models …

Due to the powerful and automatic representation capabilities, deep learning (DL)
techniques have made significant breakthroughs and progress in hyperspectral unmixing …