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...

the northwestern part of the kerman cenozoic magmatic arc (kcma) contains many areas with porphyry copper mineralization. in this research, we used the advanced space-borne thermal emission and reflection radiometer (aster) and enhanced thematic mapper plus (etm+) images of this region to map the distribution of hydrothermally altered rocks, based on their mineral assemblages. the spectral meas...

The northwestern part of the Kerman Cenozoic magmatic arc (KCMA) contains many areas with porphyry copper mineralization. In this research, we used the advanced space-borne thermal emission and reflection radiometer (ASTER) and Enhanced Thematic Mapper plus (ETM+) images of this region to map the distribution of hydrothermally altered rocks, based on their mineral assemblages. The spectral meas...

Most of the approaches to solve the unmixing problem are based on the Linear Mixing Model (LMM) which is questionable. Therefore, nonlinear spectral model is generally used to study the effects of multiple scattering in the complex surfaces. In this paper, we have demonstrated the application of Radiative Transform Equation (RTE) based Hapke multi scattering model. The Hapke model based non-lin...

Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which w...

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 ...

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...

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...

In the field of remote sensing, the unmixing of hyperspectral images is usually based on the use of a mixing model. Most existing spectral unmixing methods, used in the reflective range [0.4-2.5 μm], rely on a linear model of endmember reflectances. Nevertheless, such a model supposes the pixels at ground level to be uniformly irradiated and the scene to be flat. When considering a 3D landscape...

Hyperspectral images represent an important source of information to assess ecosystem biodiversity. In particular, plant species richness is a primary indicator of biodiversity. This paper uses spectral variance to predict vegetation richness, known as Spectral Variation Hypothesis. Hierarchical agglomerative clustering is our primary tool to retrieve clusters whose Shannon entropy should refle...