Imaging spectrometers measure electromagnetic energy scattered in their instantaneous
field view in hundreds or thousands of spectral channels with higher spectral resolution than …

Iron-bearing minerals are sensitive to a wide spectrum of natural processes and thus carry
important environmental information. In environmental magnetism, various techniques are …

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] …

Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of
high-dimensional data as it automatically extracts sparse and meaningful features from a set …

Linear spectral unmixing aims at estimating the number of pure spectral substances, also
called endmembers, their spectral signatures, and their abundance fractions in remotely …

Spectral unmixing is an important technique for remotely sensed hyperspectral data
exploitation. It amounts to identifying a set of pure spectral signatures, which are called …

Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

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 …

This paper introduces a new unsupervised hyperspectral unmixing method conceived to
linear but highly mixed hyperspectral data sets, in which the simplex of minimum volume …

This paper considers volume minimization (VolMin)-based structured matrix factorization.
VolMin is a factorization criterion that decomposes a given data matrix into a basis matrix …