Ship and Oil-Spill Detection Using the Degree of Polarization in Linear and Hybrid/Compact Dual-Pol SAR

Monitoring and detection of ships and oil spills using synthetic aperture radar (SAR) have received a considerable attention over the past few years, notably due to the wide area coverage and day and night all-weather capabilities of SAR systems. Among different polarimetric SAR modes, dual-pol SAR data are widely used for monitoring large ocean and coastal areas. The degree of polarization (DoP) is a fundamental quantity characterizing a partially polarized electromagnetic field, with significantly less computational complexity, readily adaptable for on-board implementation, compared with other well-known polarimetric discriminators. The performance of the DoP is studied for joint ship and oil-spill detection under different polarizations in hybrid/compact and linear dual-pol SAR imagery. Experiments are performed on RADARSAT-2 -band polarimetric data sets, over San Francisco Bay, and -band NASA/JPL UAVSAR data, covering the Deepwater Horizon oil spill in the Gulf of Mexico

Estimation of the Degree of Polarization for Hybrid/Compact and Linear Dual-Pol SAR Intensity Images: Principles and Applications

Analysis and comparison of linear and hybrid/compact dual-polarization (dual-pol) synthetic aperture radar (SAR) imagery have gained a wholly new importance in the last few years, in particular, with the advent of new spaceborne SARs such as the Japanese ALOS PALSAR, the Canadian RADARSAT-2, and the German TerraSAR-X. Compact polarimetry, hybrid dual-pol, and quad-pol modes are newly promoted in the literature for future SAR missions. In this paper, we investigate and compare different hybrid/compact and linear dual-pol modes in terms of the estimation of the degree of polarization (DoP). The DoP has long been recognized as one of the most important parameters characterizing a partially polarized electromagnetic wave. It can be effectively used to characterize the information content of SAR data. We study and compare the information content of the intensity data provided by different hybrid/compact and linear dual-pol SAR modes. For this purpose, we derive the joint distribution of multilook SAR intensity images. We use this distribution to derive the maximum likelihood and moment-based estimators of the DoP in hybrid/compact and linear dual-pol modes.We evaluate and compare the performance of these estimators for different modes on both synthetic and real data, which are acquired by RADARSAT-2 spaceborne and NASA/JPL airborne SAR systems, over various terrain types such as urban, vegetation, and ocean

Time-scale analysis of abrupt changes corrupted by multiplicative noise

Multiplicative Abrupt Changes (ACs) have been considered in many applications. These applications include image processing (speckle) and random communication models (fading). Previous authors have shown that the Continuous Wavelet Transform (CWT) has good detection properties for ACs in additive noise. This work applies the CWT to AC detection in multiplicative noise. CWT translation invariance allows to define an AC signature. The problem then becomes signature detection in the time-scale domain. A second-order contrast criterion is defined as a measure of detection performance. This criterion depends upon the first- and second-order moments of the multiplicative process's CWT. An optimal wavelet (maximizing the contrast) is derived for an ideal step in white multiplicative noise. This wavelet is asymptotically optimal for smooth changes and can be approximated for small AC amplitudes by the Haar wavelet. Linear and quadratic suboptimal signature-based detectors are also studied. Closed-form threshold expressions are given as functions of the false alarm probability for three of the detectors. Detection performance is characterized using Receiver Operating Characteristic (ROC) curves computed from Monte-Carlo simulations

The reform of European securities settlement systems : Towards an integrated financial market

The European Central Bank (ECB) will offer to banks in 2013 an european shared platform for securities settlement, named TARGET 2 Securities (T2S), in order to open the national financial markets. The financial crisis did not change the ECB agenda. This paper develops a spatial competition model to understand the impact of this new organisation on european post-trading services. We analyse the incentives of the Central Securities Depositaries (CSD) to move to T2S when they become competitors in the market for settlement services and remain in a monopoly position for depository services. Settlement and depository services are complementary goods, because banks have to pay for these two services to buy or sell a security. We show that such a reform should induce a decrease in the settlement price and more generally in post-trading prices, but that prices depend strongly on market organisation. Under certain conditions, partial adhesion would make prices increase. This configuration appears as a Nash equilibrium. As CSDs are free to adhere to T2S, the ECB might be forced to regulate.Post-trading organisation; securities settlement; depositary services; compatibility

Conversion Numérique-Analogique sélective d'un signal passe-bande soumis à des interférences

National audienceCet article propose une méthode qui permet une conversion numérique-analogique sélective d’un processus aléatoire passe-bande soumis à des interférences. Cette méthode permet d’effectuer simultanément la conversion numérique-analogique du signal et le rejet de l’interférence à partir des échantillons du processus observé : aucun démodulation préalable du processus passe-bande n’est nécessaire et le filtrage est effectué dans le domaine temporel grâce à l’expression explicite des coefficients du filtre. La méthode se base sur l’utilisation d’un schéma d’échantillonnage périodique non uniforme appelé PNS2 (pour Periodic Nonuniform Sampling d’ordre 2) qui utilise deux séquences d’échantillonnage périodique entrelacées. Des formules appropriées sont établies afin de reconstruire le signal, permettant également de supprimer l’interférence grâce à un filtrage sélectif. L’observation sur une fenêtre de taille infinie (nombre infini d’échantillons) mène à une reconstruction exacte. Cependant, dans les applications, la conversion numérique-analogique est généralement pratiquée en temps réel à l’aide d’une fenêtre d’observation glissante et de taille finie (nombre fini d’échantillons). Ainsi les formules de reconstruction doivent avoir un taux de convergence élevé. Cet article propose donc des formules avec différents taux de convergence grâce à l’utilisation de filtres avec des fonctions de tranfert de régularité croissante. Des simulations se basant sur la variation de différents paramètres expérimentaux nous ont permis de tester la méthode

Image watermarking based on the space/spatial-frequency analysis and Hermite functions expansion

International audienceAn image watermarking scheme that combines Hermite functions expansion and space/spatial-frequency analysis is proposed. In the first step, the Hermite functions expansion is employed to select busy regions for watermark embedding. In the second step, the space/spatial-frequency representation and Hermite functions expansion are combined to design the imperceptible watermark, using the host local frequency content. The Hermite expansion has been done by using the fast Hermite projection method. Recursive realization of Hermite functions significantly speeds up the algorithms for regions selection and watermark design. The watermark detection is performed within the space/spatial-frequency domain. The detection performance is increased due to the high information redundancy in that domain in comparison with the space or frequency domains, respectively. The performance of the proposed procedure has been tested experimentally for different watermark strengths, i.e., for different values of the peak signal-to-noise ratio (PSNR). The proposed approach provides high detection performance even for high PSNR values. It offers a good compromise between detection performance (including the robustness to a wide variety of common attacks) and imperceptibility

Robust fusion of multi-band images with different spatial and spectral resolutions for change detection

Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations, the only images available may be those acquired through different kinds of sensors. More precisely, this paper addresses the problem of detecting changes between two multiband optical images characterized by different spatial and spectral resolutions. This sensor dissimilarity introduces additional issues in the context of operational change detection. To alleviate these issues, classical change detection methods are applied after independent preprocessing steps (e.g., resampling) used to get the same spatial and spectral resolutions for the pair of observed images. Nevertheless, these preprocessing steps tend to throw away relevant information. Conversely, in this paper, we propose a method that more effectively uses the available information by modeling the two observed images as spatial and spectral versions of two (unobserved) latent images characterized by the same high spatial and high spectral resolutions. As they cover the same scene, these latent images are expected to be globally similar except for possible changes in sparse spatial locations. Thus, the change detection task is envisioned through a robust multiband image fusion method, which enforces the differences between the estimated latent images to be spatially sparse. This robust fusion problem is formulated as an inverse problem, which is iteratively solved using an efficient block-coordinate descent algorithm. The proposed method is applied to real panchromatic, multispectral, and hyperspectral images with simulated realistic and real changes. A comparison with state-of-the-art change detection methods evidences the accuracy of the proposed strategy

Lifespan Modeling of Insulation Materials for Low Voltage Machines: films and twisted pairs

This paper deals with the modeling of insulation material lifespan in a partial discharge regime. Accelerated aging tests are carried out to determine the lifespan of polyesterimide insulation films under different various stress conditions. The insulation lifespan logarithm is modeled as a function of different factors: the electrical stress logarithms and of an exponential form of the temperature. The model parameters, or so-called factor effects, is estimated on a training set. The significance of the factors is evaluated through the analysis of variance (ANOVA). In a first step, the design of experiment method (DoE) is considered. The associated lifespan model is linear with respect to the factors. This method is well-known for reducing the number of experiments while providing a good accuracy. In a second step, the response surface method (RSm) is considered. This method takes also into account some second order terms and thus possible interactions between the stress factors. Performance of the two methods are analyzed and compared on a test set. </p

Surrogate-based diagnosis of mechanical faults in induction motor from stator current measurements

This paper focuses on induction motor monitoring based on stator current measurements. The diagnosis aims at identifying the mechanical faults related to either airgap eccentricity or load torque oscillation. The airgap eccentricity (respectively load torque oscillation) essentially results in an amplitude (respectively phase) modulation of the stator current. Classical spectral analysis allows for the detection but not for the discrimination of these modulations. Time-frequency representations, such as the spectrogram or the Wigner distribution, provide appropriate signatures for fault discrimination. This paper proposes to perform the decision task from the time-frequency representation using the surrogate data technique. In a deterministic context, the phase and amplitude modulations can be considered as non-stationarities since they correspond to time-variations of the signal spectral content. The detection of a modulation is expressed as a binary hypothesis test. The null hypothesis corresponds to a signal without modulation. Stationarized/unmodulated replicas of the observed (possibly modulated) signal are obtained by phase randomization of its Fourier transform. These so-called surrogates provide a reference for the null hypothesis. The observed signal is then compared to these surrogates using appropriate distances in the time-frequency domain. A one-class classifier may be used considering the surrogates as a learning set. This classifier detects outliers corresponding to the modulation and thus to the failures. Moreover, this technique provides the information concerning the predominant type of modulation. This diagnosis method will be tested on simulated and experimental signals