Statistical analysis of hyper-spectral data: a non-Gaussian approach

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

How university’s activities support the development of students’ entrepreneurial abilities: case of Slovenia and Croatia

The paper reports how the offered university activities support the development of students’ entrepreneurship abilities. Data were collected from 306 students from Slovenian and 609 students from Croatian universities. The study reduces the gap between theoretical researches about the academic entrepreneurship education and individual empirical studies about the student’s estimation of the offered academic activities for development of their entrepreneurial abilities. The empirical research revealed differences in Slovenian and Croatian students’ perception about (a) needed academic activities and (b) significance of the offered university activities, for the development of their entrepreneurial abilities. Additionally, the results reveal that the impact of students’ gender and study level on their perception about the importance of the offered academic activities is not significant for most of the considered activities. The main practical implication is focused on further improvement of universities’ entrepreneurship education programs through selection and utilization of activities which can fill in the recognized gaps between the students’ needed and the offered academic activities for the development of students’ entrepreneurial abilities

Improved Learning-Based Approach for Atmospheric Compensation of VNIR-SWIR Hyperspectral Data

In this work, the Learning-Based Approach to Atmospheric Compensation (LBAC) of hyperspectral data proposed by Acito et al. is extended. LBAC makes use of machine learning methods to directly estimate the spectral reflectance from the at-sensor radiance accounting for the variability induced by one or more unknown atmospheric parameters and by-passing their estimation. LBAC training is obtained by exploiting a spectral reflectance library and accounting for the effects of both the atmosphere and the noise. However, depending on the spectral library adopted, some specific spectra may be reconstructed with lower accuracy. To overcome this drawback, two solutions are proposed referring to two application scenarios. The former deals with small and rare anomalous pixels with unknown reflectance and could be of interest in many applications such as man-made targets detection. It leverages the strengths of LBAC and those of the empirical line method (ELM). The second scenario refers to the case of materials with a priori known spectral reflectance and is defined for applications such as mining exploration and contaminant detection. It directly acts on the training phase of LBAC by introducing the spectra of interest in the generation of the training set. An extensive analysis is carried out on simulated data to test the effectiveness of the proposed solutions, to discuss their strengths and weakness, and to compare them with a classical physics-based approach. Results on a real hyperspectral image acquired by an airborne sensor provide a demonstration of the effectiveness of the proposed strategies in a real application environment

Atmospheric Column Water Vapor Retrieval From Hyperspectral VNIR Data Based on Low-Rank Subspace Projection

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 algorithm is the Atmospheric Precorrected Differential Absorption Technique (APDA). 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 (LRSWE). 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

Unsupervised Atmospheric Compensation of airborne hyperspectral images in the VNIR spectral range

Atmospheric compensation is a fundamental and critical step for quantitative exploitation of hyperspectral data. It is the means by which the reflectance of an object/material is estimated from the measured at-sensor radiance. Such reflectance is the inherent signature that is used to identify various materials in a monitored scene. Atmospheric compensation is quite complex and is hampered by the large amount of uncontrollable variables that play a role: just think about the spatial variability of some atmospheric constituents such as water vapor and aerosols, or to the rapidly spatially varying effects of the radiation coming from adjacent areas. Though, in principle, some atmospheric parameters and radiometric quantities such as solar irradiance and sky irradiance can be measured during the flight, in practice such measures are rarely available in an operational framework or are taken at a single point of the surface ignoring their spatial variation. Thus, a prompt quantitative exploitation of hyperspectral data for operational purposes, such as material identification and object detection, requires unsupervised and accurate atmospheric compensation procedures that can learn from the image itself the parameters of the inversion model and follow their variability within the scene. In this framework, we present a new unsupervised methodology for atmospheric compensation of airborne hyperspectral images in the Visible and Near Infra Red spectral range. The proposed methodology relies on a radiative transfer model accounting for the adjacency effect and allows the estimation of relevant atmospheric parameters. Specifically, it embeds two new algorithms for the estimation of 1) aerosol and atmospheric visibility and 2) the water vapor content of the atmosphere accounting for the spatial variability of such a parameter. The two algorithms significantly differ from those adopted by existing state of art approaches or in commercial packages like FLAASH and ATCOR. In this paper, we present the detailed description of the new atmospheric compensation methodology, and we analyze the results provided by the algorithm over real data

Mitigating the impact of signal-dependent noise on hyperspectral target detection

A pre-processing procedure can diminish the data noise from new-generation hyperspectral sensors, thus minimizing negative impacts on target detection algorithms

Modelli di probabilita' per la classificazione e la rivelazione in immagini iperspettrali

Nell'affrontare problemi di classificazione e rilevazione di immagini iperspettrali utilizzando metodi statistici, la definizione di un modello di probabilità per le grandezze impiegate nelle regole di decisione degli algoritmi permette di prevederne le prestazioni e derivare i criteri di progetto. In questo lavoro si analizza statisticamente una grandezza utilizzata da diversi algoritmi e proporzionale alla distanza di Mahalanobis. In particolare, si propongono cinque modelli e per ognuno si definisce una metodologia per stimare i valori dei parametri che garantiscono l'adattamento alla distribuzione dei dati reali. In relazione ad un algoritmo di rivelazione si discutono le deviazioni delle prestazioni rispetto al modello gaussiano. -------------------------------------------------------------------------------

A new algorithm for robust estimation of the signal subspace in hyperspectral images in presence of rare signal components

This paper deals with the problem of signal subspace estimation for dimensionality reduction (DR) in hyperspectral images in the presence of rare pixels, i.e., pixels that are scarcely represented in the image and containing spectral components that are linearly independent of the background. Most of the classical methods proposed in the literature are based on the analysis of second-order statistics (SOS), which are weakly influenced by the rare signals. Therefore, such techniques estimate the signal subspace addressing mostly the background and ignoring the presence of rare pixels. This may reduce the target/background spectral contrast, thus decreasing the detection performance when DR is adopted as preprocessing task in small-target detection applications. In this paper, a new robust algorithm, namely, robust signal subspace estimation (RSSE), is developed, which preserves both abundant and rare signal components. It combines the analysis of SOS and a recent approach based on the analysis of the l 2 infin norm. The novel contribution of this paper is twofold. First, the RSSE algorithm is presented, which includes a new iterative procedure to derive the signal subspace and an original statistical method to estimate the data dimensionality. Second, an ad hoc simulation strategy is proposed to assess the performance of signal subspace estimation methods in the presence of rare signal components. The procedure is adopted to compare the RSSE algorithm with a classical technique based on the analysis of SOS. The results obtained by applying the two methods on a real Airborne Visible Infrared Imaging Spectrometer hyperspectral image are also presented and discussed

Detection Peformance Loss due to Jitter in Naval IRST Systems

Infrared search and track (IRST) systems based on 3-D velocity filters are quite sensitive to the random motion that remains after egomotion compensation has occurred (jitter). These systems are designed to detect small targets at long range and they often employ track-before-detect (TBD) strategies to integrate the target signal along consecutive frames. Jitter causes frame misalignment and leads to a reduction of the detection performance. We analyze the effects of jitter on the false alarm and detection probabilities and on the detection range. The analytical expressions here derived are a valuable tool both in the analysis and synthesis of IRST systems based on 3-D velocity filters