A Multi-layer Perceptron based Non-linear Mixture Model to estimate class abundance from mixed pixels

Sub-pixel classification is essential for the successful description of many land cover (LC) features with spatial resolution less than the size of the image pixels. A commonly used approach for sub-pixel classification is linear mixture models (LMM). Even though, LMM have shown acceptable results, pragmatically, linear mixtures do not exist. A non-linear mixture model, therefore, may better describe the resultant mixture spectra for endmember (pure pixel) distribution. In this paper, we propose a new methodology for inferring LC fractions by a process called automatic linear-nonlinear mixture model (AL-NLMM). AL-NLMM is a three step process where the endmembers are first derived from an automated algorithm. These endmembers are used by the LMM in the second step that provides abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual proportions are fed to multi-layer perceptron (MLP) architecture as input to train the neurons which further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. AL-NLMM is validated on computer simulated hyperspectral data of 200 bands. Validation of the output showed overall RMSE of 0.0089±0.0022 with LMM and 0.0030±0.0001 with the MLP based AL-NLMM, when compared to actual class proportions indicating that individual class abundances obtained from AL-NLMM are very close to the real observations.

Assimilation of endmember variability in spectral for urban land cover extraction mixture analysis

Variable Endmember Constrained Least Square (VECLS) technique is proposed to account endmember variability in the linear mixture model by incorporating the variance for each class, the signals of which varies from pixel to pixel due to change in urban land cover (LC) structures. VECLS is first tested with a computer simulated three class endmember considering four bands having small, medium and large variability with three different spatial resolutions. The technique is next validated with real datasets of IKONOS, Landsat ETM+ and MODIS. The results show that correlation between actual and estimated proportion is higher by an average of 0.25 for the artificial datasets compared to a situation where variability is not considered. With IKONOS, Landsat ETM+ and MODIS data, the average correlation increased by 0.15 for 2 and 3 classes and by 0.19 for 4 classes, when compared to single endmember per class. (C) 2013 COSPAR. Published by Elsevier Ltd. All rights reserved.

Performance analysis using equivariant kernel density estimator in nonlinear mixture

This paper investigates the performance analysis of separation of mutually independent sources in nonlinear models. The nonlinear mapping constituted by an unsupervised linear mixture is followed by an unknown and invertible nonlinear distortion, are found in many signal processing cases. Generally, blind separation of sources from their nonlinear mixtures is rather difficult. We propose using a kernel density estimator incorporated with equivariant gradient analysis to separate the sources with nonlinear distortion. The kernel density estimator parameters of which are iteratively updated to minimize the output independence expressed as a mutual information criterion. The equivariant gradient algorithm has the form of nonlinear decorrelation to perform the convergence analysis. Experiments are proposed to illustrate these results.

Tailoring of Structural Morphology of Silver Nanowires in Electrochemical Growth

Noble metal such as Ag normally exists in an fcc crystal structure. However as the size of the material is decreased to nanometer lengthscales, a structural transformation from that of its bulk state can be expected with new atomic arrangements due to competition between internal packing and minimization of surface energy. In many previous studies, it has been shown that silver nanowires (AGNWs) grown inside anodic alumina (AAO) templates by ac or dc electrochemical deposition from silver salts or complexes, adopt fcc structure and below some critical diameter ∼ 20 nm they may acquire hcp structure at low temperature. This is, however, critically dependant on the nature of confinement, as AgNWs grown inside nanotube confinement with subnanometer diameter have been reported to have fcc structure. Hence the question of the crystal structure of metal nanowires under combined influence of confinement, temperature and deposition condition remains open. In this abstract we show that the alternative crystal structures of AGNWs at room temperature can be achieved with electrochemical growth processes under specific conditions determined by the deposition parameters and nature of confinement. We fabricated AgNWs of 4H hexagonal structure with diameters 30 – 80 nm inside polycarbonate (PC) templates with a modified dc electrodeposition technique, where the nanowires were grown at deposition potentials as low as 10 mV in 2 M silver nitrate solution[1]. We call this low-potential electrodeposition (LPED) since the electrodeposition process occurs at potential much less than the standard Nernst potential (770 mV) of silver. Two types of electrodes were used – stainless steel and sputtered thin Pt film, neither of which had any influence on the crystal structure of the nanowires. EDS elemental analysis showed the nanowires to consist only of silver. Although the precise atomic dynamics during the LPED process is unclear at present, we investigated this with HRTEM (high-resolution transmission electron microscopy) characterization of nanowires grown over various deposition times, as well as electrical conductivity measurements. These experiments indicate that nanowire growth does not occur through a three-dimensional diffusion controlled process, as proposed for conventional over-potential deposition, but follow a novel instantaneous linear growth mechanism. Further experiments showed that, (a) conventional electrochemical growth at a small over-potential in a 2 mM AgNO3 solution yields nanowires with expected fcc structure inside the same PC templates, and (2) no nanowire was observed under the LPED conditions inside hard AAO templates, indicating that LPED-growth process, and hcp structure of the corresponding nanowires depend on deposition parameters, as well as nature of confinement.

Spectral invariant behavior of zenith radiance around cloud edges observed by ARM SWS

The ARM Shortwave Spectrometer (SWS) measures zenith radiance at 418 wavelengths between 350 and 2170 nm. Because of its 1-sec sampling resolution, the SWS provides a unique capability to study the transition zone between cloudy and clear sky areas. A spectral invariant behavior is found between ratios of zenith radiance spectra during the transition from cloudy to cloud-free. This behavior suggests that the spectral signature of the transition zone is a linear mixture between the two extremes (definitely cloudy and definitely clear). The weighting function of the linear mixture is a wavelength-independent characteristic of the transition zone. It is shown that the transition zone spectrum is fully determined by this function and zenith radiance spectra of clear and cloudy regions. An important result of these discoveries is that high temporal resolution radiance measurements in the clear-to-cloud transition zone can be well approximated by lower temporal resolution measurements plus linear interpolation.

Anomalous Behavior of Linear Hydrocarbon Chains in Water-DMSO Binary Mixture at Low DMSO Concentration

We find that at a mole fraction 0.05 of DMSO (x(DMSO) = 0.05) in aqueous solution, a linear hydrocarbon chain of intermediate length (n = 30-40) adopts the most stable collapsed conformation. In pure water, the same chain exhibits an intermittent oscillation between the collapsed and the extended coiled conformations. Even when the mole fraction of DMSO in the bulk is 0.05, the concentration of the same in the first hydration layer around the hydrocarbon of chain length 30 (n = 30) is as large as 17%. Formation of such hydrophobic environment around the hydrocarbon chain may be viewed as the reason for the collapsed conformation gaining additional stability. We find a second anomalous behavior to emerge near x(DMSO) = 0.15, due to a chain-like aggregation of the methyl groups of DMSO in water that lowers the relative concentration of the DMSO molecules in the hydration layer. We further find that as the concentration of DMSO is gradually increased, it progressively attains the extended coiled structure as the stable conformation. Although Flory-Huggins theory (for binary mixture solvent) fails to predict the anomaly at x(DMSO) = 0.05, it seems to capture the essence of the anomaly at 0.15.

Adaptive Mixture Modelling Metropolis Methods for Bayesian Analysis of Non-linear State-Space Models.

We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis-Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. These mixtures are propagated through the non-linearities using an accurate, local mixture approximation method, and we use a regenerating procedure to deal with potential degeneracy of mixture components. This provides accurate, direct approximations to sequential filtering and retrospective smoothing distributions, and hence a useful construction of global Metropolis proposal distributions for simulation of posteriors for the set of states. This analysis is embedded within a Gibbs sampler to include uncertain fixed parameters. We give an example motivated by an application in systems biology. Supplemental materials provide an example based on a stochastic volatility model as well as MATLAB code.

Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression

Affiliation: Institut de recherche en immunologie et en cancérologie, Université de Montréal