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] …

Adaptive networks are suitable for decentralized inference tasks. Recent works have
intensively studied distributed optimization problems in the case where the nodes have to …

Spectral unmixing is an important issue to analyze remotely sensed hyperspectral data.
Although the linear mixture model has obvious practical advantages, there are many …

Sparsity-based unmixing algorithms, exploiting the sparseness property of the abundances,
have recently been proposed with promising performances. However, these algorithms are …

Because of recent advances of hyperspectral imaging technology with hundreds of spectral
bands being used for data acquisition, how to handle enormous data volumes using …

Hyperspectral unmixing (HU) is a crucial signal processing procedure to identify the
underlying materials (or endmembers) and their corresponding proportions (or abundances) …

Endmember finding has become increasingly important in hyperspectral data exploitation
because endmembers can be used to specify unknown particular spectral classes. Pixel …

Hyperspectral data unmixing aims at identifying the components (endmembers) of an
observed surface and at determining their fractional abundances inside each pixel area …

Recently, many nonlinear spectral unmixing algorithms that use various bilinear mixture
models (BMMs) have been proposed. However, the high computational complexity and …

Nonnegative matrix factorization (NMF) is a powerful class of feature extraction techniques
that has been successfully applied in many fields, particularly in signal and image …