Umberto Amato

The σ-IASI code for the calculation of infrared atmospheric radiance and its derivatives

This paper describes a new fast line-by-line radiative transfer scheme which computes top of the atmosphere spectral radiance and its Jacobians with respect to any set of geophysical parameters both for clear and cloudy sky, and presents the software which implements the procedure. The performance of the code has been evaluated with respect to accuracy and speediness through a comparison with a state-of-art line-by-line radiative transfer model. The new code is well suited for nadir viewing satellite and airplane infrared sensors with a sampling rate in the range 0.1–2 cm 1 .  2002 Elsevier Science Ltd. All rights reserved.

Dimension reduction in functional regression with applications

Two dimensional reduction regression methods to predict a scalar response from a discretized sample path of a continuous time covariate process are presented. The methods take into account the functional nature of the predictor and are both based on appropriate wavelet decompositions. Using such decompositions, prediction methods are devised that are similar to minimum average variance estimation (MAVE) or functional sliced inverse regression (FSIR). Their practical implementation is described, together with their application both to simulated and on real data analyzing three calibration examples of near infrared spectra.

Segmentation of magnetic resonance brain images through discriminant analysis

Segmentation (tissue classification) of medical images obtained from a magnetic resonance (MR) system is a primary step in most applications of medical image post-processing. This paper describes nonparametric discriminant analysis methods to segment multispectral MR images of the brain. Starting from routinely available spin-lattice relaxation time, spin-spin relaxation time, and proton density weighted images (T1w, T2w, PDw), the proposed family of statistical methods is based on: (i) a transform of the images into components that are statistically independent from each other; (ii) a nonparametric estimate of probability density functions of each tissue starting from a training set; (iii) a classic Bayes 0-1 classification rule. Experiments based on a computer built brain phantom (brainweb) and on eight real patient data sets are shown. A comparison with parametric discriminant analysis is also reported. The capability of nonparametric discriminant analysis in improving brain tissue classification of parametric methods is demonstrated. Finally, an assessment of the role of multispectrality in classifying brain tissues is discussed.

Experimental Approach to the Selection of the Components in the Minimum Noise Fraction

An experimental method to select the number of principal components in minimum noise fraction (MNF) is proposed to process images measured by imagery sensors onboard aircraft or satellites. The method is based on an experimental measurement by spectrometers in dark conditions from which noise structure can be estimated. To represent typical land conditions and atmospheric variability, a significative data set of synthetic noise-free images based on real Multispectral Infrared and Visible Imaging Spectrometer images is built. To this purpose, a subset of spectra is selected within some public libraries that well represent the simulated images. By coupling these synthetic images and estimated noise, the optimal number of components in MNF can be obtained. In order to have an objective (fully data driven) procedure, some criteria are proposed, and the results are validated to estimate the number of components without relying on ancillary data. The whole procedure is made computationally feasible by some simplifications that are introduced. A comparison with a state-of-the-art algorithm for estimating the optimal number of components is also made.

A Hybrid With Cross-Entropy Method and Sequential Quadratic Programming to Solve Economic Load Dispatch Problem

This paper presents a new hybrid approach integrating the cross-entropy (CE) algorithm and the sequential quadratic programming (SQP) technique to solve the economic load dispatch (ELD) problem related to electrical power generating units. Due to the introduction of the valve-point effect in the ELD objective function, the optimization task requires tools appropriate for a nonconvex optimization landscape. In this respect, we employ the CE approach, which constructs a random sequence of solutions probabilistically converging to a near-optimal solution and, thus, facilitating the exploration capability. Additionally, to fine-tune the solution in promising basins of attraction, the SQP algorithm is invoked, which performs a local search. Despite its reliance on a global heuristic scheme, CE-SQP is vested with fast convergence capability, which may entail its use for online power dispatch. The effectiveness and the robustness of the proposed method in comparison with several state-of-the-art approaches have been demonstrated with four standard test systems that are widely reported in the ELD literature.

Wavelet kernel penalized estimation for non-equispaced design regression

The paper considers regression problems with univariate design points. The design points are irregular and no assumptions on their distribution are imposed. The regression function is retrieved by a wavelet based reproducing kernel Hilbert space (RKHS) technique with the penalty equal to the sum of blockwise RKHS norms. In order to simplify numerical optimization, the problem is replaced by an equivalent quadratic minimization problem with an additional penalty term. The computational algorithm is described in detail and is implemented with both the sets of simulated and real data. Comparison with existing methods showed that the technique suggested in the paper does not oversmooth the function and is superior in terms of the mean squared error. It is also demonstrated that under additional assumptions on design points the method achieves asymptotic optimality in a wide range of Besov spaces.

Effect of apodization on the retrieval of geophysical parameters from Fourier-transform spectrometers

The problem of the effect of apodization on the retrieval of geophysical parameters from infrared radiances recorded by Fourier transform spectrometers has been analytically and numerically addressed. Exploiting a matrix representation of apodization, we first derive a general analytical expression for the apodized covariance matrix and then show that apodization, when properly applied, has no effect on retrievals. The methodology has been applied to investigate the effect of Gaussian apodization on the Infrared Atmospheric Sounding Interferometer currently under development at the laboratories of the French Space Agency.

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.

Inverting for geophysical parameters from IMG radiances

Radiances observed by the Interferometric Monitor for Greenhouse gases sounder have been used to retrieve temperature, water vapor, and ozone profiles. It is shown that the sounder allows one to simultaneously retrieve stable solutions for temperature and water vapor. Once water vapor and temperature have been retrieved, ozone profile may be estimated on the same fine vertical mesh as water vapor and temperature. Comparison among calculated and observed radiances shows good agreement in several parts of the thermal band which is sensed by the sounder. A slight discrepancy is observed in the wing region of the 720-cm/sup -1/ CO/sub 2/ Q-branch, whereas the most severe form of disagreement is seen in the 6.7-/spl mu/m vibrational H/sub 2/O absorption band. Nevertheless, suitable spectral ranges may be identified which yield accurate and stable inversions for temperature, water vapor, and ozone.

Adaptive wavelet series estimation in separable nonparametric regression models

It is well-known that multivariate curve estimation suffers from the “curse of dimensionality.” However, reasonable estimators are possible, even in several dimensions, under appropriate restrictions on the complexity of the curve. In the present paper we explore how much appropriate wavelet estimators can exploit a typical restriction on the curve such as additivity. We first propose an adaptive and simultaneous estimation procedure for all additive components in additive regression models and discuss rate of convergence results and data-dependent truncation rules for wavelet series estimators. To speed up computation we then introduce a wavelet version of functional ANOVA algorithm for additive regression models and propose a regularization algorithm which guarantees an adaptive solution to the multivariate estimation problem. Some simulations indicate that wavelets methods complement nicely the existing methodology for nonparametric multivariate curve estimation.