Change Detection for Multi-Polarization SAR

Il presente lavoro di tesi si inquadra nel contesto della change detection in immagini SAR. In particolare, la procedura di change detection si pone l'obiettivo di individuare i cambiamenti che avvengono in un area di interesse in un certo intervallo temporale a partire da una coppia coregistrata di immagini SAR rappresentative della scena di interesse nell'intervallo temporale considerato. Presupposto fondamentale delle analisi di change detection è che un cambiamento nella superficie osservata da un sensore corrisponda a un cambiamento di risposta nei dati telerilevati sensibilmente più rilevante rispetto ai cambiamenti dovuti ad altri fattori, quali le condizioni atmosferiche al momento delle acquisizioni, o l'umidità dei suoli o ancora differenze nelle condizioni di acquisizione dei dati telerilevati. Al fine di perseguire tale scopo, in letteratura aperta, vengono considerati due principali approcci noti come change detection incoerente e change detection coerente. A differenza dell'approccio coerente, nella change detection incoerente si cerca di rivelare variazioni del livello di potenza media di una determinata scena sfruttando solo le informazioni di intensità delle immagini disponibili trascurando così le informazioni di fase. L'obiettivo principale che si è inteso perseguire nel lavoro svolto, è stato quello di considerare un nuovo approccio per il problema della change detection basato sulla teoria dell'invarianza nei test di ipotesi considerando un modello di dati polarimetrico, ovvero, supponendo di avere diversità in termini di polarizzazione dei dati relativi alla scena di interesse. In particolare, il principio di invarianza consente, in fase di progetto, di forzare alcune interessanti proprietà della statistica di decisione permettendo di concentrarsi su regole decisionali che mostrano alcune simmetrie naturali implicando importanti proprietà pratiche come il comportamento CFAR (Constant False Alarm Ratio). Inoltre, l'uso del principio di invarianza porta ad una riduzione dei dati, in quanto, tutti i test invarianti possono essere espressi in termini di una statistica, chiamata massimale invariante, che organizza i dati originali in classi di equivalenza. In fase di sviluppo, dopo aver trovato un massimale invariante per il problema in esame si è osservato che il rivelatore invariante ottimo ad esso associato non è UMPI (Uniformly Most Powerful Invariant), quindi, non praticamente realizzabile. Di conseguenza, sono stati determinati alcuni ricevitori invarianti sub-ottimi, le cui prestazioni sono state valutate sia su dati simulati che su immagini SAR reali ad alta risoluzione. Successivamente, si è considerata una particolare struttura diagonale a blocchi per la matrice di covarianza polarimetrica. Imponendo tale vincolo al problema della change detection è stata introdotta una nuova regola di decisione basata sul criterio GLRT (Generalized Likelihood Ratio Test). Inoltre, seguendo un tale approccio, si è dimostrato che il ricevitore sintetizzato garantisce il comportamento CFAR. Tuttavia, nei problemi di change detection, può accadere che le due immagini vengano acquisite con angoli di vista leggermente diversi, o, che le proprietà di propagazione del mezzo cambino tra le due acquisizioni. Tali effetti, possono essere portati in conto in fase di progetto considerando un fattore di scala in termini di intensità. Se il sistema di rilevamento non è stato progettato per tenere conto di tali effetti, il disallineamento in termini di intensità tra le due immagini può introdurre dei falsi allarme con conseguente perdita in termini di capacità rivelazione del sistema di change detection. Per tale motivo, si introduce la possibilità di tenere conto di possibili variazioni di scala al fine di rendere più robusti i ricevitori sintetizzati. Nello specifico, una volta formulato analiticamente il problema considerato, viene sintetizzato il GLRT per i casi in cui si considerino due e tre canali e polarimetrici. Inoltre, utilizzando il principio di invarianza, in primo luogo si dimostra che i ricevitori basati sul GLRT soddisfano la proprietà CFAR, ed, in secondo luogo, vengono proposte alcune ulteriori regole decisionali invarianti. In tutti i casi di studio considerati, le analisi delle prestazioni sono state effettuate utilizzando sia dati simulati che immagini SAR reali ad alta risoluzione. In entrambi i casi, i risultati ottenuti hanno mostrato che alcuni degli algoritmi sviluppati forniscono prestazioni soddisfacenti in termini di capacità di rivelazione dei cambiamenti nell'area di interesse e dunque, rappresentano una buona soluzione per affrontare il problema di change detection

Headache disorders as risk factors for sleep disturbances in school aged children.

Several epidemiological studies have shown the presence of comorbidity between various types of sleep disorders and different headache subtypes. Migraine without aura is a sensitive risk factor for disorders of initiating and maintaining sleep (odds ratio (OR) 8.2500), and chronic tension-type headache for sleep breathing disorders (OR 15.231), but headache disorder is a cumulative risk factor for disorders of excessive somnolence (OR 15.061). This result has not been reported in the clinical literature. © Springer-Verlag Italia 2005

Forcing scale invariance in multipolarization SAR change detection

This paper considers the problem of coherent (in the sense that both amplitudes and relative phases of the polarimetric returns are used to construct the decision statistic) multi-polarization SAR change detec- tion starting from the availability of image pairs exhibiting possible power mismatches/miscalibrations. The principle of invariance is used to characterize the class of scale-invariant decision rules which are insensitive to power mismatches and ensure the Constant False Alarm Rate (CFAR) property. A maximal invariant statistic is derived together with the induced maximal invariant in the parameter space which significantly compress the data/parameters domain. A Generalized Likelihood Ratio Test (GLRT) is synthesized both for the cases of two- and three-polarimetric channels. Interestingly, for the two-channel case, it is based on the comparison of the condition number of a data-dependent matrix with a suitable threshold. Some additional invariant decision rules are also proposed. The performance of the considered scale-invariant structures is compared to those from two non- invariant counterparts using both simulated and real radar data. The results highlight the robustness of the proposed method and the performance tradeoff involve

Correlation analysis between foveal avascular zone and near peripheral retinal hypoperfusion in multiple sclerosis: a wide field optical coherence tomography angiography study

The identification of non-invasive biomarkers to investigate and monitor retinal structural and vascular changes in multiple sclerosis (MS) represents an interesting source of debate. Until now optical coherence tomography angiography (OCTA) evaluated the foveal avascular zone (FAZ) and areas of retinal non-perfusion only in the macular region in MS patients. It could be interesting to identify possible biomarkers, useful in assessing the ischemic areas also in the near peripheral retina, since FAZ enlargement and the areas of peripheral retinal non-perfusions share common pathogenic processes. In this cross-sectional study, we investigated the correlation between the FAZ area and retinal vessel density (VD) in the near peripheral retina by new wide-field optical coherence tomography angiography (OCTA) in patients affected by relapsing-remitting multiple sclerosis (RR-MS). Moreover, we compared the FAZ area and the VD of superficial and deep capillary plexuses in the fovea region and in the near peripheral retina (6.4 x 6.4 mm) between RR-MS patients and healthy controls by means of a Solix full-range OCTA. Last, we also detected the changes in structural OCT parameters (ganglion cell complex and retinal nerve fiber layer). Thirty-three eyes of 33 RR-MS patients and 35 eyes of 35 healthy controls were enrolled. RR-MS patients showed a lower VD in the superficial capillary plexus and a significant increase in the FAZ area compared with controls. The deep capillary plexus revealed a reduced VD although not statistically significant in patients with respect to controls. In the patients' group, the FAZ area showed significantly negative correlations with VD of superficial capillary plexuses in the foveal and whole region, while the FAZ area did not negatively correlate with the VD of the deep capillary plexus. The significant correlations among OCTA parameters could demonstrate the FAZ area as a possible biomarker for assessing the perfusion status in the near peripheral retina, useful in RR-MS management. These findings could confirm the role of vascular dysfunction in the pathogenetic mechanisms of MS

Stacking sequences in composite laminates through design optimization

AbstractComposites are experiencing a new era. The spatial resolution at which is to date possible to build up complex architectured microstructures through additive manufacturing-based and sintering of powder metals 3D printing techniques, as well as the recent improvements in both filament winding and automated fiber deposition processes, are opening new unforeseeable scenarios for applying optimization strategies to the design of high-performance structures and metamaterials that could previously be only theoretically conceived. Motivated by these new possibilities, the present work, by combining computational methods, analytical approaches and experimental analysis, shows how finite element Design Optimization algorithms can be ad hoc rewritten by identifying as design variables the orientation of the reinforcing fibers in each ply of a layered structure for redesigning fiber-reinforced composites exhibiting at the same time high stiffness and toughening, two features generally in competition each other. To highlight the flexibility and the effectiveness of the proposed strategy, after a brief recalling of the essential theoretical remarks and the implemented procedure, selected example applications are finally illustrated on laminated plates under different boundary conditions, cylindrical layered shells with varying curvature subjected to point loads and composite tubes made of carbon fiber-reinforced polymers, recently employed as structural components in advanced aerospace engineering applications

Atorvastatin combined to interferon to verify the efficacy (ACTIVE) in relapsing-remitting active multiple sclerosis patients: a longitudinal controlled trial of combination therapy.

A large body of evidence suggests that, besides their cholesterol-lowering effect, statins exert anti-inflammatory action. Consequently, statins may have therapeutic potential in immune-mediated disorders such as multiple sclerosis. Our objectives were to determine safety, tolerability and efficacy of low-dose atorvastatin plus high-dose interferon beta-1a in multiple sclerosis patients responding poorly to interferon beta-1a alone. Relapsing–remitting multiple sclerosis patients, aged 18–50 years, with contrast-enhanced lesions or relapses while on therapy with interferon beta-1a 44 mg (three times weekly) for 12 months, were randomized to combination therapy (interferon+atorvastatin 20mg per day; group A) or interferon alone (group B) for 24 months. Patients underwent blood analysis and clinical assessment with the Expanded Disability Status Scale every 3 months, and brain gadolinium-enhanced magnetic resonance imaging at screening, and 12 and 24 months thereafter. Primary outcome measure was contrast-enhanced lesion number. Secondary outcome measures were number of relapses, EDSS variation and safety laboratory data. Forty-five patients were randomized to group A (n 1⁄4 21) or B (n 1⁄4 24). At 24 months, group A had significantly fewer contrast-enhanced lesions versus baseline (p 1⁄4 0.007) and significantly fewer relapses versus the two pre-randomization years (p < 0.001). At survival analysis, the risk for a 1-point EDSS increase was slightly higher in group B than in group A (p 1⁄4 0.053). Low-dose atorvastatin may be beneficial, as add-on therapy, in poor responders to high-dose interferon beta-1a alone

Performance prediction of the coherent radar detector on measured UAVs data

This paper presents measurements of Radar Cross Section (RCS) of five Unmanned Aerial Vehicles (UAVs), comprising both consumer grade and professional small drones, collected in a semi-controlled environment as a function of azimuth aspect angle, polarization and frequency in the range 8.2-18 GHz. A first-order statistical analysis of the measured RCSs is firstly reported prior to assessing the radar detection performance on both measured and bespoke simulated data (leveraging the results of the developed statistical analysis), including, as benchmark terms, the curves for non-fluctuating and Rayleigh fluctuating targets

Radar detection performance prediction using measured UAVs RCS data

This paper presents measurements of Radar Cross Section (RCS) of five Unmanned Aerial Vehicles (UAVs), comprising both consumer grade and professional small drones, collected in a semi-controlled environment as a function of azimuth aspect angle, polarization and frequency in the range 8.2-18 GHz. The experimental setup and the data pre-processing, which include coherent background subtraction and range gating procedures, are illustrated in detail. Furthermore, a thorough description of the calibration process, which is based on the substitution method, is discussed. Then, a first-order statistical analysis of the measured RCSs is provided by means of the Cramér-von Mises (CVM) distance and the Kolmogorov-Smirnov (KS) test. Finally, radar detection performance is assessed on both measured and bespoke simulated data (leveraging the results of the developed statistical analysis), including, as benchmark terms, the curves for non-fluctuating and Rayleigh fluctuating targets

Radar detection performance via frequency agility using measured UAVs RCS data

This paper addresses radar detection performance prediction (via measured data) for drone targets using a frequency agility-based incoherent (square-law) detector. To this end, a preliminary statistical analysis of the integrated Radar Cross Section (RCS) resulting from frequency agile pulses is carried out for drones of different sizes and characteristics, using data acquired in a semi-controlled environment for distinct frequencies, angles, and polarizations. The analysis involves fitting the integrated RCS measurements with commonly used one-parametric and two-parametric probability distributions and leverages the Cramér-von Mises distance and the Kolmogorov Smirnov test. Results show that the Gamma distribution appears to accurately model the resulting fluctuations. Hence, the impact of integration and frequency agility on the RCS fluctuation dispersion is studied. Finally, detection performance of the incoherent square-law detector is assessed for different target and radar parameters, using both measured and simulated data drawn from a Gamma distribution whose parameters follow the preliminary RCS statistical analysis. The results highlight a good agreement between simulated and measurement-based curves

An Approximate Regularized ML Approach to Censor Outliers in Gaussian Radar Data

This paper considers the problem of censoring outliers from the secondary dataset in a radar scenario where the sample support is limited. To this end, the generalized regularized likelihood function (GRLF) criterion is used and the corresponding regularized maximum likelihood (RML) estimate of the outlier subset is derived. Since the exact RML estimate involves the solution of a combinatorial optimization problem, a reduced complexity but approximate RML (ARML) procedure is also designed. As to the selection of the regularization parameter, both the expected likelihood (EL) principle and the cross-validation (CV) technique are exploited. At the analysis stage, the performance of the RML and ARML procedure is evaluated based on simulated data in comparison with some previously proposed methods. The results highlight that the RML and ARML algorithm achieves, in general, a satisfactory performance level whereas the previously proposed techniques often experience some performance degradation when the volume of training data is dramatically limited