Maria Francesca Carfora

Objective algorithms for the aerosol problem

Retrieval of the aerosol size distribution from optical measurements at ground level is well known to be a difficult problem. Nowadays objective techniques that can give a solution without the intervention of the researcher do not exist. We propose several objective methods that are well based in the mathematical and physical points of view. Their accuracy is evaluated and the top performance of the objective inversion techniques is presented. Moreover physical and experimental suggestions can be drawn to improve the accuracy. Inversions with experimental optical depths are also shown.

Numerical methods for retrieving aerosol size distributions from optical measurements of solar radiation

Abstract Retrieving aerosol size distributions from ground measurements is an ill-posed problem. Several methods have been developed to “regularize” it, giving a stable approximation of its solution. The aim of this paper is to review the techniques usually adopted and present some new approaches. The case of solutions retrieved both in their parametric and non-parametric form is considered. For all the considered methods the problem of choosing an optimal level of regularization is also discussed. Finally, numerical results comparing some inversion techniques in a real case are presented.

Statistical Classification for Assessing PRISMA Hyperspectral Potential for Agricultural Land Use

The upcoming launch of the next generation of hyperspectral satellites (PRISMA, EnMap, HyspIRI, etc.) will meet the increasing demand for the availability/accessibility of hyperspectral information on agricultural land use from the agriculture community. To this purpose, algorithms for the classification of remotely sensed images are here considered for agricultural monitoring of cultivated area, exploiting remotely sensed high spectral resolution images. Classification is accomplished by procedures based on discriminant analysis tools that well suit hyperspectrality, circumventing what in statistics is called “the curse of dimensionality”. As a byproduct of classification, a full assessment of the spectral bands of the sensor is obtained, ranking them with the purpose of understanding their role in segmentation and classification. The methodology has been validated on two independent image datasets gathered by the MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) sensor for which ground validations were available. A comparison with the popular multiclass SVM (Support Vector Machines) classifier is also presented. Results show that a good classification (minimum global success rate 95% through all experiments) is achieved by using the 10 spectral bands selected as the most discriminant by the proposed procedure; moreover, it also appears that nonparametric techniques generally outperform parametric ones. The present study confirms that the new generation of hyperspectral satellite data like PRISMA can ripen an end-user application for agricultural land-use of cultivated area.

Descent Algorithms on Oblique Manifold for Source-Adaptive ICA Contrast

A Riemannian manifold optimization strategy is proposed to facilitate the relaxation of the orthonormality constraint in a more natural way in the course of performing independent component analysis (ICA) that employs a mutual information-based source-adaptive contrast function. Despite the extensive development of manifold techniques catering to the orthonormality constraint, only a limited number of works have been dedicated to oblique manifold (OB) algorithms to intrinsically handle the normality constraint, which has been empirically shown to be superior to other Riemannian and Euclidean approaches. Imposing the normality constraint implicitly, in line with the ICA definition, essentially guarantees a substantial improvement in the solution accuracy, by way of increased degrees of freedom while searching for an optimal unmixing ICA matrix, in contrast with the orthonormality constraint. Designs of the steepest descent, conjugate gradient with Hager-Zhang or a hybrid update parameter, quasi-Newton, and cost-effective quasi-Newton methods intended for OB are presented in this paper. Their performance is validated using natural images and systematically compared with the popular state-of-the-art approaches in order to assess the performance effects of the choice of algorithm and the use of a Riemannian rather than Euclidean framework. We surmount the computational challenge associated with the direct estimation of the source densities using the improved fast Gauss transform in the evaluation of the contrast function and its gradient. The proposed OB schemes may find applications in the offline image/signal analysis, wherein, on one hand, the computational overhead can be tolerated, and, on the other, the solution quality holds paramount interest.

A leaky integrate-and-fire model with adaptation for the generation of a spike train.

A model is proposed to describe the spike-frequency adaptation observed in many neuronal systems. We assume that adaptation is mainly due to a calcium-activated potassium current, and we consider two coupled stochastic differential equations for which an analytical approach combined with simulation techniques and numerical methods allow to obtain both qualitative and quantitative results about asymptotic mean firing rate, mean calcium concentration and the firing probability density. A related algorithm, based on the Hazard Rate Method, is also devised and described.

GAUSS-DIFFUSION PROCESSES FOR MODELING THE DYNAMICS OF A COUPLE OF INTERACTING NEURONS

With the aim to describe the interaction between a couple of neurons a stochastic model is proposed and formalized. In such a model, maintaining statements of the Leaky Integrate-and-Fire framework, we include a random component in the synaptic current, whose role is to modify the equilibrium point of the membrane potential of one of the two neurons and when a spike of the other one occurs it is turned on. The initial and after spike reset positions do not allow to identify the inter-spike intervals with the corresponding first passage times. However, we are able to apply some well-known results for the first passage time problem for the Ornstein-Uhlenbeck process in order to obtain (i) an approximation of the probability density function of the inter-spike intervals in one-way-type interaction and (ii) an approximation of the tail of the probability density function of the inter-spike intervals in the mutual interaction. Such an approximation is admissible for small instantaneous firing rates of both neurons.

Semi-Lagrangian treatment of advection on the sphere with accurate spatial and temporal approximations

This paper is devoted to the numerical solution of the transport equation on the sphere, aimed at the implementation of accurate spatial interpolation procedures and of accurate reconstruction of the characteristic lines in a semi-Lagrangian framework. Since nonregular grid and pole singularity can both affect accuracy of the numerical approximation, proper account is taken for these problems. It is shown on a literature test case that accurate spatial approximation highly improves the accuracy of the method, having a greater impact than accurate temporal approximation. The algorithms can be introduced into general circulation models, where accurate temporal approximation is expected to play a major role.

Cloud Detection of MODIS Multispectral Images

AbstractMethods coming from statistics and pattern recognition to estimate the cloud mask from radiance measured by visible and infrared sensors on board satellites are gaining greater consideration for their ability to properly exploit the increasing number of channels available with current and next-generation sensors. Endowed with physical arguments, they give rise to robust methods for accurately estimating the cloud mask. Application of such classification methods to Moderate Resolution Imaging Spectroradiometer (MODIS) data is discussed in this paper. Three different types of MODIS datasets are considered: synthetic (radiance is simulated by proper radiative transfer models); annotated (real MODIS data labeled by a meteorologist as clear or cloudy); and real MODIS data, whose truth is obtained from the official MODIS cloud mask product. A full assessment of the MODIS spectral bands is performed, aimed at understanding the role of the spectral bands in detecting clouds and at achieving top performanc...

Evaluation of supervised methods for the classification of major tissues and subcortical structures in multispectral brain magnetic resonance images

This work investigates the capability of supervised classification methods in detecting both major tissues and subcortical structures using multispectral brain magnetic resonance images. First, by means of a realistic digital brain phantom, we investigated the classification performance of various Discriminant Analysis methods, K-Nearest Neighbor and Support Vector Machine. Then, using phantom and real data, we quantitatively assessed the benefits of integrating anatomical information in the classification, in the form of voxels coordinates as additional features to the intensities or tissue probabilistic atlases as priors. In addition we tested the effect of spatial correlations between neighboring voxels and image denoising. For each brain tissue we measured the classification performance in terms of global agreement percentage, false positive and false negative rates and kappa coefficient. The effectiveness of integrating spatial information or a tissue probabilistic atlas has been demonstrated for the aim of accurately classifying brain magnetic resonance images.

Effectiveness of the operator splitting for solving the atmospherical shallow water equations

A semi‐implicit semi‐Lagrangian mixed finite‐difference finite‐volume model for the shallow water equations on a rotating sphere is considered. The main features of the model are the finite‐volume approach for the continuity equation and the vectorial treatment of the momentum equation. Pressure and Coriolis terms in the momentum equation and velocity in the continuity equation are treated semi‐implicitly. Discretization of this model led to the introducion, in a previous paper, of a splitting technique which highly reduces the computational effort for the numerical solution. In this paper we solve the full set of equations, without splitting, introducing an ad hoc algorithm. A von Neumann stability analysis of this scheme is performed to establish the unconditional stability of the new proposed method. Finally, we compare the efficiency of the two approaches by numerical experiments on a standard test problem. Results show that, due to the devised algorithm, the solution of the full system of equations is much more accurate while slightly increasing the computational cost.