The composite nature of remotely sensed spectral information often masks diagnostic
spectral features and hampers the detailed identification and mapping of targeted …

Hyperspectral sensors enable the study of the chemical properties of scene materials
remotely for the purpose of identification, detection, and chemical composition analysis of …

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

Convex optimization problems are common in hyperspectral unmixing. Examples are the
constrained least squares (CLS) problem used to compute the fractional abundances in a …

Nonlinear models have recently shown interesting properties for spectral unmixing. This
paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for …

This paper presents a new linear hyperspectral unmixing method of the minimum volume
class, termed simplex identification via split augmented Lagrangian (SISAL). Following …

In this work, many of the fundamental and advanced spectral processing methods available
to geologic remote sensing are reviewed. A novel categorization scheme is proposed that …

This paper presents a new method of minimum volume class for hyperspectral unmixing,
termed minimum volume simplex analysis (MVSA). The underlying mixing model is linear; …

This paper studies a fully Bayesian algorithm for endmember extraction and abundance
estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed …

This paper presents a nonlinear mixing model for hyperspectral image unmixing. The
proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral …