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 …

Hyperspectral unmixing realizes the unmixing by determining the pure substances
(endmembers) and their proportion (abundances) in the mixing pixels. Most of the previous …

Hyperspectral sparse unmixing aims at modeling pixels of hyperspectral image as a linear
combination of a subset of a prior spectral library. Over the past years, spectral library has …

Mixed pixels are the main reason for the low accuracy of traditional remote sensing
applications. Hyperspectral image unmixing can explore the sub-pixel information of mixed …

In a previous paper, we introduced (i) a specific hyperspectral mixing model for the sea
bottom, based on a detailed physical analysis that includes the adjacency effect, and (ii) an …

Convolutional neural-network-based autoencoders, which can integrate the spatial
correlation between pixels well, have been broadly used for hyperspectral unmixing and …

Spectral unmixing (SU) is a significant preprocessing task for handling hyperspectral images
(HSI), but its process is affected by nonlinearity and spectral variability (SV). Currently, SV is …

Hyperspectral unmixing allows representing mixed pixels as a set of pure materials
weighted by their abundances. Spectral features alone are often insufficient, so it is common …

X-ray microspectroscopic techniques are essential for studying morphological and chemical
changes in materials, providing high-resolution structural and spectroscopic information …

Nonlinear hyperspectral unmixing has recently received considerable attention, as linear
mixture models do not lead to an acceptable resolution in some problems. In fact, most …