Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the
last four decades from being a sparse research tool into a commodity product available to a …

Vegetation cover fraction (fCover) and related quantities are basic yet critical vegetation
structure variables in various disciplines and applications. Ground-and aerial-based …

Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from
spectral variability, making it difficult for spectral unmixing to accurately estimate abundance …

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 …

Feature learning technologies using convolutional neural networks (CNNs) have shown
superior performance over traditional hand-crafted feature extraction algorithms. However, a …

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

The spectral signatures of the materials contained in hyperspectral images, also called
endmembers (EMs), can be significantly affected by variations in atmospheric, illumination …

Convolutional neural network (CNN) is well known for its capability of feature learning and
has made revolutionary achievements in many applications, such as scene recognition and …

Blind hyperspectral unmixing (HU), also known as unsupervised HU, is one of the most
prominent research topics in signal processing (SP) for hyperspectral remote sensing [1],[2] …

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 …