Nonlinear unmixing of hyperspectral images: Models and algorithms

When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM). However, the LMM may be not valid, and other nonlinear models need to be considered, for instance, when there are multiscattering effects or intimate interactions. Consequently, over the last few years, several significant contributions have been proposed to overcome the limitations inherent in the LMM. In this article, we present an overview of recent advances in nonlinear unmixing modeling

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery

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 components contaminated by an additive white Gaussian noise. These nonlinear functions are approximated using polynomial functions leading to a polynomial postnonlinear mixing model. A Bayesian algorithm and optimization methods are proposed to estimate the parameters involved in the model. The performance of the unmixing strategies is evaluated by simulations conducted on synthetic and real data

Neural network methods for one-to-many multi-valued mapping problems

An investigation of the applicability of neural network-based methods in predicting the values of multiple parameters, given the value of a single parameter within a particular problem domain is presented. In this context, the input parameter may be an important source of variation that is related with a complex mapping function to the remaining sources of variation within a multivariate distribution. The definition of the relationship between the variables of a multivariate distribution and a single source of variation allows the estimation of the values of multiple variables given the value of the single variable, addressing in that way an ill-conditioned one-to-many mapping problem. As part of our investigation, two problem domains are considered: predicting the values of individual stock shares, given the value of the general index, and predicting the grades received by high school pupils, given the grade for a single course or the average grade. With our work, the performance of standard neural network-based methods and in particular multilayer perceptrons (MLPs), radial basis functions (RBFs), mixture density networks (MDNs) and a latent variable method, the general topographic mapping (GTM), is compared. According to the results, MLPs and RBFs outperform MDNs and the GTM for these one-to-many mapping problems

Estimation of in-situ fluid properties from the combined interpretation of nuclear, dielectric, optical, and magnetic resonance measurements

During the last few decades, the quantification of hydrocarbon pore volume from borehole measurements has been widely studied for reservoir descriptions. Relatively less effort has been devoted to estimating in-situ fluid properties because (1) acquiring fluid samples is expensive, (2) reservoir fluids are a complex mixture of various miscible and non-miscible phases, and (3) they depend on environmental factors such as temperature and pressure. This dissertation investigates the properties of fluid mixtures based on various manifestations of their electromagnetic properties from the MHz to the THz frequency ranges. A variety of fluids, including water, alcohol, alkane, aromatics, cyclics, ether, and their mixtures, are analyzed with both laboratory experiments and numerical simulations. A new method is introduced to quantify in-situ hydrocarbon properties from borehole nuclear measurements. The inversion-based estimation method allows depth-continuous assessment of compositional gradients at in-situ conditions and provides thermodynamically consistent interpretations of reservoir fluids that depend greatly on phase behavior. Applications of this interpretation method to measurements acquired in two field examples, including one in a gas-oil transition zone, yielded reliable and verifiable hydrocarbon compositions. Dielectric properties of polar liquid mixtures were analyzed in the frequency range from 20 MHz to 20 GHz at ambient conditions. The Havriliak-Negami (HN) model was adapted for the estimation of dielectric permittivity and relaxation time. These experimental dielectric properties were compared to Molecular Dynamics (MD) simulations. Additionally, thermodynamic properties, including excess enthalpy, density, number of hydrogen bonds, and effective self-diffusion coefficient, were computed to cross-validate experimental results. Properties predicted from MD simulations are in excellent agreement with experimental measurements. The three most common optical spectroscopy techniques, i.e. Near Infrared (NIR), Infrared, and Raman, were applied for the estimation of compositions and physical properties of liquid mixtures. Several analytical techniques, including Principal Component Analysis (PCA), Radial Basis Functions (RBF), Partial Least-Squares Regression (PLSR), and Artificial Neural Networks (ANN), were separately implemented for each spectrum to build correlations between spectral data and properties of liquid mixtures. Results show that the proposed methods yield prediction errors from 1.5% to 22.2% smaller than those obtained with standard multivariate methods. Furthermore, the errors can be decreased by combining NIR, Infrared, and Raman spectroscopy measurements. Lastly, the ¹H NMR longitudinal relaxation properties of various liquid mixtures were examined with the objective of detecting individual components. Relaxation times and diffusion coefficients obtained via MD simulations for these mixtures are in agreement with experimental data. Also, the ¹H-¹H dipole-dipole relaxations for fluid mixtures were decomposed into the relaxations emanate from the intramolecular and intermolecular interactions. The quantification of intermolecular interactions between the same molecules and different molecules reveals how much each component contributes to the total NMR longitudinal relaxation of the mixture as well as the level of interactions between different fluids. Both experimental and numerical simulation results documented in this dissertation indicate that selecting measurement techniques that can capture the physical property of interest and maximize the physical contrasts between different components is important for reliable and accurate in-situ fluid identificationPetroleum and Geosystems Engineerin

Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies

© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio