Accademia Navale di Livorno

Livorno, Italy

Accademia Navale di Livorno

Livorno, Italy

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Berti P.,University of Modena and Reggio Emilia | Pratelli L.,Accademia Navale di Livorno | Rigo P.,Universita Of Pavia
Advances in Intelligent Systems and Computing | Year: 2017

Let (Xn) be a sequence of random variables, adapted to a filtration (Gn), and let μn = (1/n) ∑n i =1 δXi and an(・) = P(Xn +1∈ ・ | Gn) be the empirical and the predictive measures. We focus on ||μn − an|| = supB ∈D |μn(B) − an(B)|, where D is a class of measurable sets. Conditions for ||μn − an|| → 0, almost surely or in probability, are given. Also, to determine the rate of convergence, the asymptotic behavior of rn ||μn − an|| is investigated for suitable constants rn. Special attention is paid to rn = √n. The sequence (Xn) is exchangeable or, more generally, conditionally identically distributed. © Springer International Publishing Switzerland 2017.


Becherini G.,Accademia Navale di Livorno | Di Fraia S.,Accademia Navale di Livorno | Genovesi G.,GSD S.r.l. EMC Laboratory | Petri A.,GSD S.r.l. EMC Laboratory | And 3 more authors.
IEEE Transactions on Plasma Science | Year: 2011

We present the results of an experimental analysis aimed at investigating the electromagnetic (EM) emission during rail launcher operation. In order to obtain such data, an experimental setup was assembled in a shielded semi-anechoic chamber, consisting of a pulsed power source-unit, a power coaxial cable and a rail launcher prototype. Several experiments were performed for different operating conditions and results were repeatable. Finally, we provide a qualitative modeling of some sources of electromagnetic transients and we discuss basic aspects driving the EM emission phenomena in railgun systems. © 2010 IEEE.


Rossi A.,University of Pisa | Acito N.,Accademia Navale di Livorno | Diani M.,University of Pisa | Luison C.,Altran GmbH | And 2 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2013

Modern thermal cameras acquire IR images with a high dynamic range because they have to sense with high thermal resolution the great temperature changes of monitored scenarios in specific surveillance applications. Initially developed for visible light images and recently extended for display of IR images, high dynamic range compression (HDRC) techniques aim at furnishing plain images to human operators for a first intuitive comprehension of the sensed scenario without altering the features of IR images. In this context, the maritime scenario represents a challenging case to test and develop HDRC strategies since images collected for surveillance at sea are typically characterized by high thermal gradients among the background scene and classes of objects at different temperatures. In the development of a new IRST system, Selex ES assembled a demonstrator equipped with modern thermal cameras and planned a measurement campaign on a maritime scenario so as to collect IR sequences in different operating conditions. This has led to build up a case record of situations suitable to test HDRC techniques. In this work, a survey of HDRC approaches is introduced pointing out advantages and drawbacks with focus on strategies specifically designed to display IR images. A detailed analysis of the performance is discussed in order to address the task of visualization with reference to typical issues of IR maritime images, such as robustness to the horizon effect and displaying of very warm objects and flat areas. © 2013 SPIE.


Acito N.,Accademia Navale di Livorno | Resta S.,University of Pisa | Diani M.,University of Pisa | Corsini G.,University of Pisa
Optical Engineering | Year: 2013

A novel technique for anomalous change detection (ACD) in hyperspectral images is presented. The technique embeds a strategy robust to residual misregistration errors that typically affect data collected by airborne platforms. Furthermore, the proposed technique mitigates the negative effects due to random noise, by means of a band selection technique aimed at discarding spectral channels whose useful signal content is low compared to the noise contribution. Band selection is performed on a per-pixel basis by exploiting the estimates of the noise variance accounting also for the presence of the signal-dependent noise component. Real data collected by a new generation airborne hyperspectral camera on a complex urban scenario are considered to test the proposed method. Performance evaluation shows the effectiveness of the proposed approach with respect to a previously proposed ACD algorithm based on the same similarity measure. © 2013 Society of Photo-Optical Instrumentation Engineers (SPIE).


Acito N.,Accademia Navale di Livorno | Rossi A.,University of Pisa | Diani M.,University of Pisa | Corsini G.,University of Pisa
Optical Engineering | Year: 2011

A well-established scheme for target detection in infrared (IR) surveillance systems consists of applying a suitable decision rule on the images with background clutter previously removed. Background removal is accomplished by subtracting, from the original image, the estimate of the spatially varying background signal obtained by a background estimation algorithm (BEA). The overall target detection performance is strongly influenced by the effectiveness of the employed BEA. Particularly, the BEA and its design parameters should be chosen so as to get an accurate estimate of the background signal and to avoid biases caused by the possible presence of targets (target leakage). In this work, we present a novel method for the choice and setting of the best performing background removal technique for the detection of dim point targets. The proposed procedure is based on the simulation of dim targets implanted on an acquired sample image representing the scenario of interest. The choice of the best performing BEA is made by exploring the performance of the detection scheme for several configurations of the characteristic parameters of the BEAs. The effectiveness of the BEA selection procedure is evaluated in two case studies where real image sequences acquired by IR cameras are employed. The results confirm the benefits introduced by the proposed technique. Indeed, the performance of the IR detection system with the BEA tuned according to the proposed selection criterion is improved in that the number of false alarms is reduced up to 2 orders of magnitude compared with BEAs in other common configurations. © 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).


Rossi A.,University of Pisa | Acito N.,Accademia Navale di Livorno | Diani M.,University of Pisa | Corsini G.,University of Pisa
Journal of Real-Time Image Processing | Year: 2014

In the field of hyperspectral image processing, anomaly detection (AD) is a deeply investigated task whose goal is to find objects in the image that are anomalous with respect to the background. In many operational scenarios, detection, classification and identification of anomalous spectral pixels have to be performed in real time to quickly furnish information for decision-making. In this framework, many studies concern the design of computationally efficient AD algorithms for hyperspectral images in order to assure real-time or nearly real-time processing. In this work, a sub-class of anomaly detection algorithms is considered, i.e., those algorithms aimed at detecting small rare objects that are anomalous with respect to their local background. Among such techniques, one of the most established is the Reed-Xiaoli (RX) algorithm, which is based on a local Gaussian assumption for background clutter and locally estimates its parameters by means of the pixels inside a window around the pixel under test (PUT). In the literature, the RX decision rule has been employed to develop computationally efficient algorithms tested in real-time systems. Initially, a recursive block-based parameter estimation procedure was adopted that makes the RX processing and the detection performance differ from those of the original RX. More recently, an update strategy has been proposed which relies on a line-by-line processing without altering the RX detection statistic. In this work, the above-mentioned RX real-time oriented techniques have been improved using a linear algebra-based strategy to efficiently update the inverse covariance matrix thus avoiding its computation and inversion for each pixel of the hyperspectral image. The proposed strategy has been deeply discussed pointing out the benefits introduced on the two analyzed architectures in terms of overall number of elementary operations required. The results show the benefits of the new strategy with respect to the original architectures. © 2012 Springer-Verlag Berlin Heidelberg.


Rossi A.,University of Pisa | Acito N.,Accademia Navale di Livorno | Diani M.,University of Pisa | Corsini G.,University of Pisa
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2014

In surveillance applications, tracking a specific target by means of subsequent acquisitions over the monitored area is of great interest. Multitemporal HyperSpectral Images (HSIs) are particularly suitable for this application. Multiple HSIs of the same scene collected at different times can be exploited to detect changes using anomalous change detection (ACD) techniques. Moreover, spectral matching (SM) is a valuable tool for detecting the target spectrum within HSIs collected at different times (target rediscovery-TR). Depending on the monitored area and the specific target of interest, TR can be a challenging task. In fact, it may happen that the target has spectral features similar to those of uninteresting objects in the scene and the use of SM techniques without additional information can generate too many misleading detections. We introduce a new TR strategy aimed at mitigating the number of alarms encountered in complex scenarios. The proposed detection strategy combines the SM approach with the unsupervised ACD strategy. We focus on rediscovery of moving targets in airborne HSIs collected on the same complex area. False alarms mitigation is achieved by exploiting both the target spectral features and the temporal variations of its position. For this purpose, SM is performed only on those pixels that have undergone changes within multiple acquisitions. Results obtained applying the proposed scheme on real HSIs are presented and discussed. The results show the effectiveness of the fusion of spectral and multitemporal analysis to improve TR performance in complex scenarios. © 2014 SPIE.


Rossi A.,University of Pisa | Acito N.,Accademia Navale di Livorno | Diani M.,University of Pisa | Corsini G.,University of Pisa
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2012

Modern InfraRed (IR) cameras have High Dynamic Range (HDR) and excellent sensitivity. They collect images using a number of bits much higher than the 8-bits used in displays or than those effectively perceived by the human visual system. In IR imagery, suitable techniques to display HDR images are therefore required in order to improve the visibility of the details while preserving the global perception of the scene. Visualization of HDR images has already been widely studied for visible-light images. In the IR framework only a few works have been proposed which tightly depend on the operating scenario and on the application of interest. In most cases such works have been obtained by modifications of methods proposed for visible light images rather than by developing visualization techniques taking into account the specific mechanism of IR image formation. In the literature, the techniques developed to display HDR images are mainly based on two approaches: contrast enhancement (CE)-oriented techniques and dynamic range compression (DRC)-oriented techniques. The former operate on image contrast to increase the perceptibility of details. The latter reduce the signal dynamic thus attenuating the large-scale intensity changes that do not contain relevant information. In addition, some of the proposed methods for HDR take advantage of both the approaches. In this work, a DRC approach is considered for visualization of HDR-IR images of maritime scenarios. A new method is presented that exploits clustering information and maps the output image according to the information content of each cluster by means of a suitable weighting function. The effectiveness of the presented technique is analyzed using IR images of a maritime scenario acquired in two different case studies. Moreover, the output images obtained with the proposed method are compared with those given by techniques previously proposed for visualization of IR images. The results show the effectiveness of the proposed technique in terms of details enhancement, robustness against the horizon effect and presence of very warm objects. © 2012 SPIE.


Acito N.,Accademia Navale di Livorno | Resta S.,University of Pisa | Diani M.,University of Pisa | Corsini G.,University of Pisa
Optical Engineering | Year: 2012

We propose a novel method to estimate the first- and second-order statistics of the residual misregistration noise (RMR), which severely affects the performance of anomalous change detection techniques. Depending on the specific distribution of the RMR, the estimates allow for precisely defining the size of the uncertainty window, which is crucial when dealing with misregistration noise, as in the local coregistration adjustment approach. The technique is based on a sequential strategy that exploits the well-known scale-invariant feature transform (SIFT) algorithm cascaded with the minimum covariance determinant algorithm. The SIFT procedure was originally developed to work on gray-level images. The proposed method adapts the SIFT procedure to hyperspectral images so as to exploit the complementary information content of the numerous spectral channels, further improving the robustness of the outliers filtering by means of a highly robust estimator of multivariate location. The approach has been tested on different real hyperspectral datasets with very high spatial resolution. The analysis highlighted the effectiveness of the proposed strategy, providing reliable and very accurate estimation of the RMR statistics. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE).


Rossi A.,University of Pisa | Acito N.,Accademia Navale di Livorno | Diani M.,University of Pisa | Corsini G.,University of Pisa
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2012

In remote sensing, hyperspectral sensors are effectively used for target detection and recognition because of their high spectral resolution that allows discrimination of different materials in the sensed scene. When a priori information about the spectrum of the targets of interest is not available, target detection turns into anomaly detection (AD), i.e. searching for objects that are anomalous with respect to the scene background. In the field of AD, anomalies can be generally associated to observations that statistically move away from background clutter, being this latter intended as a local neighborhood surrounding the observed pixel or as a large part of the image. In this context, many efforts have been put to reduce the computational load of AD algorithms so as to furnish information for real-time decision making. In this work, a sub-class of AD methods is considered that aim at detecting small rare objects that are anomalous with respect to their local background. Such techniques not only are characterized by mathematical tractability but also allow the design of real-time strategies for AD. Within these methods, one of the most-established anomaly detectors is the RX algorithm which is based on a local Gaussian model for background modeling. In the literature, the RX decision rule has been employed to develop computationally efficient algorithms implemented in real-time systems. In this work, a survey of computationally efficient methods to implement the RX detector is presented where advanced algebraic strategies are exploited to speed up the estimate of the covariance matrix and of its inverse. The comparison of the overall number of operations required by the different implementations of the RX algorithms is given and discussed by varying the RX parameters in order to show the computational improvements achieved with the introduced algebraic strategy. © 2012 SPIE.

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