The pixels of remote images often contain more than one distinct material (mixed pixels),
and so their spectra are characterized by a mixture of spectral signals. Since 1971, a shared …

Third-order tensors have been widely used in hyperspectral remote sensing because of their
ability to maintain the 3-D structure of hyperspectral images. In recent years, hyperspectral …

In the remote sensing context spectral unmixing is a technique to decompose a mixed pixel
into two fundamental representatives: endmembers and abundances. In this paper, a novel …

Nonnegative matrix factorization (NMF) has been one of the most widely used techniques for
hyperspectral unmixing (HU), which aims at decomposing each mixed pixel into a set of …

Various improved nonnegative matrix factorization (NMF) methods have been widely used
in spectral unmixing (SU), including nonlinear versions to counter for the lower spatial …

We develop a new neural network based independent component analysis (ICA) method by
directly minimizing the dependence amongst all extracted components. Using the matrix …

The unsupervised hyperspectral unmixing (HU) technique, also known as blind HU, directly
decomposes the mixed pixels of a hyperspectral image (HSI) into a combination of …

In recent hyperspectral unmixing (HU) literature, the application of deep learning (DL) has
become more prominent, especially with the autoencoder (AE) architecture. We propose a …

Nonnegative matrix factorization (NMF) can obtain endmembers and abundances
simultaneously, and has attracted a lot of interest in hyperspectral unmixing. However, it is …

Hyperspectral imaging captures detailed spectral data for remote sensing. However, due to
the limited spatial resolution of hyperspectral sensors, each pixel of a hyperspectral image …