Assessing and Enabling Independent Component Analysis as a Hyperspectral Unmixing Approach

This paper introduces a new hyperspectral unmixing method called Dependent Component Analysis (DECA). This method decomposes a hyperspectral image into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA models the abundance fractions as mixtures of Dirichlet densities, thus ...

A method, is presented for blind unmixing spectrally resolved fluorescence lifetime images. The method is based on the combined analysis of spectral and lifetime phasors and allows unmixing of up to three components without any prior knowledge. Fractional intensities, spectra and decay curves of the individual components can be extracted with this new technique. The reliability and sensitivity ...

The analysis of hyperspectral images on the basis of the spectral decomposition of their pixels through the so called spectral unmixing process, has applications in tematic map generation, target detection and unsupervised image segmentation. The critical step is the determination of the endmembers used as the references for the unmixing process. We give a comprehensive enumeration of the metho...

This paper addresses the problem of spectral unmixing when positivity and additivity constraints are imposed on the mixing coefficients. A hierarchical Bayesian model is introduced to satisfy these two constraints. A Gibbs sampler is then proposed to generate samples distributed according to the posterior distribution of the unknown parameters associated to this Bayesian model. Simulation resul...

In this paper we employ information theoretic algorithms, previously used for separating instantaneous mixtures of sources, for separating convolved mixtures in the frequency domain. It is observed that convolved mixing in the time domain corresponds to instantaneous mixing in the frequency domain. Such mixing can be inverted using simpler and more robust algorithms than the ones recently devel...

or the length of time which the process requires. The allanite replacements in the Tan-y-Grisiau microgranite suggest that unmixing was completed before hydrothermal minerals began to crvstallize and suggests, therefore, that in some instances, at least, unmixing ma.v belong to an early, relatively high temperature post-magmatic phase' The reason for the selective replacement of K-feldspar b.v ...

This work describes the minimum volume enclosing simplex problem, which is known to be a multimodal Global Optimization problem. The problem has been used as a basis to estimate so-called endmember and abundance data in unmixing spectral data of hyperspectral sensors. This estimation problem is a big challenge. We explore the possibility of a new estimation algorithm using the minimum volume en...

Recently, many sparse approximation methods have been applied to solve spectral unmixing problems. These methods in contrast to traditional methods for spectral unmixing are designed to work with large a-prori given spectral dictionaries containing hundreds of labelled material spectra enabling to skip the expensive endmember extraction and labelling step. However, it has been shown that sparse...

This paper reports on the results from ordinary least squares and ridge regression as statistical methods, and is compared to numerical optimization methods such as the stochastic method for global optimization, simulated annealing, particle swarm optimization and limited memory Broyden-Fletcher-Goldfard-Sharon bound optimization method. We used each of the above mentioned methods in estimating...