Spectral unmixing aims at estimating the fractional abundances of pure spectral signatures
(also called endmembers) in each mixed pixel collected by a remote sensing hyperspectral …

Hyperspectral unmixing (HU) is a method used to estimate the fractional abundances
corresponding to endmembers in each of the mixed pixels in the hyperspectral remote …

Spectral unmixing is an important task in hyperspectral data exploitation. It amounts to
estimating the abundance of pure spectral constituents (endmembers) in each (possibly …

Nonnegative matrix factorization (NMF) has been recently applied to solve the hyperspectral
unmixing problem because it ensures nonnegativity and needs no assumption for the …

Spectral unmixing amounts at estimating the abundance of pure spectral signatures (called
endmembers) in each mixed pixel of a hyperspectral image, where mixed pixels arise due to …

MORE than two decades after the first efforts toward the application of spectral mixture
analysis techniques to remotely sensed data [1],[2], effective spectral unmixing still remains …

Non-negative matrix factorization (NMF) has been introduced into the field of hyperspectral
unmixing in the last ten years. To relieve the non-convex problem of NMF, different …

The N-FINDR algorithm is one of the most widely used and successfully applied methods for
automatically determining endmembers in hyperspectral image data without using a priori …

Most nonlinear mixture models and unmixing methods in the literature assume implicitly that
the degrees of multiple scatterings at each band are the same. However, it is commonly …

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