Given a spectral library, sparse unmixing aims at finding the optimal subset of endmembers
from it to model each pixel in the hyperspectral scene. However, sparse unmixing still …

The nonnegative matrix factorization (NMF) combining with spatial-spectral contextual
information is an important technique for extracting endmembers and abundances of …

The dictionary-aided sparse regression (SR) approach has recently emerged as a promising
alternative to hyperspectral unmixing in remote sensing. By using an available spectral …

Sparse unmixing has been applied on hyperspectral imagery popularly in recent years. It
assumes that every observed signature is a linear combination of just a few spectra (end …

Geostationary Ocean Color Imager (GOCI) data have been widely used in the detection and
area estimation of green algae blooms. However, due to the low spatial resolution of GOCI …

This article compares the ability of nine unmixing models, including radiative transfer (RT)
models as well as a new nonlinear unmixing approach called neighbor-band ratio unmixing …

Sparse unmixing has long been a hot research topic in the area of hyperspectral image
(HSI) analysis. Most of the traditional sparse unmixing methods usually assume to only take …

Hyperspectral linear unmixing refers to the process that separates the pixels spectra from
hyperspectral image into a collection of spectral signatures referred as endmembers and …

The goal of sparse linear hyperspectral unmixing is to determine a scanty subset of spectral
signatures of materials contained in each mixed pixel and to estimate their fractional …

Sparse unmixing models based on sparse representation theory and a sparse regression
model have been successfully applied to hyperspectral remote sensing image unmixing. To …