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We investigate the statistical modeling of hyper-spectral data. The accurate modeling of experimental data is critical in target detection and classification applications. In fact, having a statistical model that is capable of properly describing data variability leads to the derivation of the best decision strategies together with a reliable assessment of algorithm performance. Most existing classification and target detection algorithms are based on the multivariate Gaussian model which, in many cases, deviates from the true statistical behavior of hyper-spectral data. This motivated us to investigate the capability of non-Gaussian models to represent data variability in each background class. In particular, we refer to models based on elliptically contoured (EC) distributions. We consider multivariate EC-t distribution and two distinct mixture models based on EC distributions. We describe the methodology adopted for the statistical analysis and we propose a technique to automatically estimate the unknown parameters of statistical models. Finally, we discuss the results obtained by analyzing data gathered by the multispectral infrared and visible imaging spectrometer (MIVIS) sensor.

Estimation of the total column water vapor (CWV) content of the atmosphere plays an important role in the atmospheric compensation (AC) of remotely sensed hyperspectral images collected in the visible and near infrared (VNIR) spectral range. Most of the proposed CWV retrieval methods provide accurate estimates as long as other significant atmospheric parameters are known. Those parameters are not generally available and must in turn be estimated. In this article, a new approach based on deep learning is proposed that allows the estimation of CWV without the knowledge of the atmospheric visibility, the solar zenith angle, and the atmospheric point spread function (PSF). The proposed approach includes a training strategy based on synthetic data that are generated according to an accurate radiative-transfer model, and by exploiting reflectance spectral libraries and the MODTRAN radiative-transfer code. Experiments on simulated data are carried out to analyze the performance of the proposed deep neural network with reference to both aerial and satellite applications. Furthermore, an example of the results provided by the network in a real application is shown. For this purpose, the network is applied to data acquired by an airborne hyperspectral sensor operating in the VNIR spectral range. [ABSTRACT FROM AUTHOR]

This letter presents a new method to detect materials with known spectral emissivity in data acquired by longwave infrared hyperspectral sensors. The proposed approach differs from existing methods because it takes into account the uncertainty of the downwelling radiance. Such uncertainty is addressed assuming that the downwelling radiance spans a low-rank subspace whose basis matrix is learned, regardless of the analyzed image, from MODTRAN simulated spectra. The analysis, carried out over data simulated by considering different atmospheric conditions, surface temperatures, and emissivity spectra, shows the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with atmospheric correction in hyperspectral data acquired in the long-wave infrared (LWIR) spectral range. Atmospheric compensation (AC) is approached from a new perspective in that it is reformulated as an estimation problem on a low-rank subspace. Specifically, a subspace-based model is exploited to represent both the atmospheric transmittance and the upwelling radiance. Taking into account the inherent correlation between these two quantities, we adopt a subspace model that constrains them to vary in a physically consistent way. Exploiting such a model, AC is formulated as a nonlinear least-squares estimation problem. An automatic procedure is proposed to estimate the unknown parameters, which is based only on the image analysis and does not need any atmospheric measurements. Results of an extensive experimental analysis carried out on simulated data are used to discuss the performance of the algorithm with respect to different atmospheric conditions. Finally, experimental evidence over a real LWIR hyperspectral image is provided. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the temperature and emissivity separation (TES) problem from hyperspectral data acquired in the long-wave infrared region (LWIR) of the electromagnetic spectrum. We derive a general class of TES algorithms [subspace-based TES (SBTES)] relying on the assumption that the emissivity spectra of natural and man-made materials can be well represented in a given subspace of the original data space. Specifically, by exploiting the subspace representation and the Gaussian model for the noise affecting LWIR hyperspectral data, we approach TES under a statistical perspective by obtaining the maximum likelihood estimates of both the temperature and the spectral emissivity. The proposed approach originates several algorithms whose specific form depends on the particular basis matrix adopted to address the emissivity subspace. We study the performance of the presented class of algorithms and derive theoretical bounds on the accuracy of the temperature and emissivity estimators. Furthermore, by specifying two basis matrices for the emissivity subspace, we propose two different algorithms within the SBTES class. Finally, we present the results of an extensive experimental analysis carried out over simulated data to assess and compare the performance of the two presented algorithms. [ABSTRACT FROM AUTHOR]

Investigates the epitaxial molecular beam epitaxy growth of nanometric germanium layers on silicon (001) 2 X 1. Characterization methods used to control the growth of available germanium molecular beam epitaxy deposits; Details on the experiment; Results of the study.

The knowledge of atmospheric column water vapor concentration is crucial for compensating water absorption effects in remote sensing data. Several algorithms for the estimation of such a parameter were proposed in the past. One of the most effective algorithms is the atmospheric precorrected differential absorption (APDA) technique. APDA relies on a simplified radiative transfer model (RTM) that does not account for the spatial variability of the adjacency effects. In this paper, we study the impact of the simplified RTM assumption on the performance of the algorithm by exploiting a more realistic and well-established RTM. Starting from such a model, we derive a new water retrieval algorithm called low-rank subspace projection-based water estimator. It exploits the high degree of spectral correlation experienced in the reflectances of most of the existing materials. An extensive experimental analysis is carried out on simulated data in order to assess and compare the performance of the two algorithms. Simulation results allow the critical analysis of the two algorithms by highlighting their strengths and drawbacks. [ABSTRACT FROM AUTHOR]

In this paper, a methodology that aims at evaluating the effectiveness of different TES strategies is presented. The methodology takes into account the specific material of interest in the monitored scenario, sensor characteristics, and errors in the atmospheric compensation step. The methodology is proposed in order to predict and analyse algorithms performances during the planning of a remote sensing mission, aimed to discover specific materials of interest in the monitored scenario. As case study, the proposed methodology is applied to a real airborne data set of a suburban scenario. In order to perform the TES problem, three state-of-the-art algorithms, and a recently proposed one, are investigated: Temperature-Emissivity Separation '98 (TES-98) algorithm, Stepwise Refining TES (SRTES) algorithm, Linear piecewise TES (LTES) algorithm, and Optimized Smoothing TES (OSTES) algorithm. At the end, the accuracy obtained with real data, and the ones predicted by means of the proposed methodology are compared and discussed. [ABSTRACT FROM AUTHOR]

Objective: Adalimumab has proven effective in psoriasis; however, secondary failure may result from the drug's immunogenicity. Prevalence data on the immunogenicity of biologicals, and of adalimumab in particular, are highly variable. We investigated the prevalence of anti-adalimumab antibodies and the association with clinical indexes and tumour necrosis factor α (TNFα) serum levels in psoriatic patients. Design: Case-control, longitudinal. Setting: Single centre. Participants: Patient groups: I (n=20) receiving biological therapies after switching from adalimumab; II (n=30) ongoing adalimumab therapy; III (n=30) novel adalimumab therapy; IV (n=15) biological therapies other than adalimumab. Healthy subjects: (group V; n=15) never treated with immunosuppressants or biologicals. Interventions: All groups were tested at enrolment. Group II was also tested at 12 months, and group III at 1, 3, and 6 months. Primary and secondary outcome measures: Standard clinical evaluations (Psoriasis Area Severity Index (PASI)), blood samples and two-site ELISAbased measurement of serum adalimumab trough levels, anti-adalimumab antibodies and TNFα. Results: The false-positive rate was 23% for adalimumab detection and 22% for anti-adalimumab antibodies in patients naïve to adalimumab. Spurious positivity for anti-adalimumab antibodies (one-timepoint positivity in group III during follow-up) accounted for 33% of the total. The prevalence of antidrug antibodies was highest (87%) in group I patients. No correlations were found between the presence of anti-adalimumab antibodies or adalimumab levels and changes in PASI scores. Conclusions: High variability of results, high prevalence of false-positives and lack of association between anti-adalimumab antibodies and TNFα level/PASI score limit this assay's usefulness. Accurate clinical evaluation is key to early identification of treatment failures. [ABSTRACT FROM AUTHOR]

We designed a method for creating 3D models of objects and areas from two aerial images acquired from an UAV. The models are generated automatically and in real-time, and consist in dense and true-colour reconstructions of the considered areas, which give the impression to the operator to be physically present within the scene. The proposed method only needs a cheap compact camera, mounted on a small UAV. No additional instrumentation is necessary, so that the costs are very limited. The method consists of two main parts: the design of the acquisition system and the 3D reconstruction algorithm. In the first part, the choices for the acquisition geometry and for the camera parameters are optimized, in order to yield the best performance. In the second part, a reconstruction algorithm extracts the 3D model from the two acquired images, maximizing the accuracy under the real-time constraint. A test was performed in monitoring a construction yard, obtaining very promising results. Highly realistic and easy-to-interpret 3D models of objects and areas of interest were produced in less than one second, with an accuracy of about 0.5m. For its characteristics, the designed method is suitable for video-surveillance, remote sensing and monitoring, especially in those applications that require intuitive and reliable information quickly, as disasters monitoring, search and rescue and area surveillance. [ABSTRACT FROM AUTHOR]