Search

International audience; It is a challenge to develop methods which can process the PolSAR and multispectral (MS) data modalities together without losing information from either for remote sensing applications. This paper presents a study which attempts to introduce novel deep learning based remote sensing data processing frameworks that utilizes convolutional neural networks (CNNs) in both spatial and spectral domains to perform land cover (LC) classification with PolSAR-MS data. Also since earth observation remotely sensed data have usually larger spectral depth than normal camera image data, exploiting the spectral information in remote sensing (RS) data is crucial as well. In fact, convolutions in the sub-spectral space are intuitive and alternative to the process of feature selection. Recently, researchers have gained success in exploiting the spectral information of RS data, especially the hyperspectral data with CNNs. In this paper, exploitation of the spectral information in the PolSAR-MS data via a permuted localized spectral convolution along with localized spatial convolution is proposed. Further, the study in this paper also establishes the significance of performing permuted localized spectral convolutions over non-localized or localized spectral convolutions. Two models are proposed, namely a permuted local spectral convolutional network (Perm-LS-CNN) and a permuted local spectral-spatial convolutional network (Perm-LSS-CNN). These models are trained on ground truth class data points measured directly on the terrain. The evaluation of the generalization performance is done using ground truth knowledge on selected well known regions in the study areas. Comparison with other popular machine learning classifiers shows that the Perm-LSS-CNN model provides better classification results in terms of both accuracy and generalization.

International audience; In this letter, we propose a speckle removal denois-ing algorithm for synthetic aperture radar (SAR) images. The approach is based on the concept of extracting informative feature (based on the concept of multifractal decomposition of signals) from a speckle-induced SAR image and then estimating a noise-free image from the gradients restricted to those features. The experimental results show that the proposed technique not only improves the visual quality of the SAR images but also effectively preserves their texture. Comparison with the classical and state-of-the-art denoising techniques shows the advantages of the proposed scheme, both visually and quantitatively.

A new denoising technique is proposed in this study for synthetic aperture radar (SAR) images corrupted by speckle noise. The authors method extract informative features from a noisy speckled image, and then a denoised version of this image is estimated from the informative gradients, which are restricted to the features of this image. The technique of extracting features is designed on the framework of multifractal formalism followed by a reconstruction technique for the informative gradients based on the total variational (TV) regularisation framework. Experimental results demonstrate that the proposed approach is able to retain the finer details of the original image while removing noise. The superiority of the proposed approach is manifested qualitatively and quantitatively on comparing with state-of-the-art denoising techniques.

26 pages, 26 figures, 1 table, 4 appendixes, Observations of the interstellar medium (ISM) show a complex density and velocity structure, which is in part attributed to turbulence. Consequently, the multifractal formalism should be applied to observation maps of the ISM in order to characterize its turbulent and multiplicative cascade properties. However, the multifractal formalism, even in its more advanced and recent canonical versions, requires a large number of realizations of the system, which usually cannot be obtained in astronomy. We present a self-contained introduction to the multifractal formalism in a “microcanonical” version, which allows us, for the first time, to compute precise turbulence characteristic parameters from a single observational map without the need for averages in a grand ensemble of statistical observables (e.g., a temporal sequence of images). We compute the singularity exponents and the singularity spectrum for both observations and magnetohydrodynamic simulations, which include key parameters to describe turbulence in the ISM. For the observations we focus on the 250 μm Herschel map of the Musca filament. Scaling properties are investigated using spatial 2D structure functions, and we apply a two-point log-correlation magnitude analysis over various lines of the spatial observation, which is known to be directly related to the existence of a multiplicative cascade under precise conditions. It reveals a clear signature of a multiplicative cascade in Musca with an inertial range from 0.05–0.65 pc. We show that the proposed microcanonical approach provides singularity spectra that are truly scale invariant, as required to validate any method used to analyze multifractality. The obtained singularity spectrum of Musca, which is sufficiently precise for the first time, is clearly not as symmetric as usually observed in log-normal behavior. We claim that the singularity spectrum of the ISM toward Musca features a more log-Poisson shape. Since log-Poisson behavior is claimed to exist when dissipation is stronger for rare events in turbulent flows, in contrast to more homogeneous (in volume and time) dissipation events, we suggest that this deviation from log-normality could trace enhanced dissipation in rare events at small scales, which may explain, or is at least consistent with, the dominant filamentary structure in Musca. Moreover, we find that subregions in Musca tend to show different multifractal properties: While a few regions can be described by a log-normal model, other regions have singularity spectra better fitted by a log-Poisson model. This strongly suggests that different types of dynamics exist inside the Musca cloud. We note that this deviation from log-normality and these differences between subregions appear only after reducing noise features, using a sparse edge-aware algorithm, which have the tendency to “log-normalize” an observational map. Implications for the star formation process are discussed. Our study establishes fundamental tools that will be applied to other galactic clouds and simulations in forthcoming studies, This work is supported by the GENESIS project (GENeration and Evolution of Structure in the ISm), via the french ANR and the german DFG through grant numbers ANR-16-CE92-0035-01 and DFG1591/2-1. N. Schneider acknowledges support by the German Deutsche Forschungsgemeinschaft, DFG project number SFB 956. L. Bonne acknowledges support by the Région Nouvelle-Aquitaine, France., With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

The Bay of Biscay and the English Channel, in the North-eastern Atlantic, are considered as a natural laboratory to explore the coastal dynamics at different spatial and temporal scales. In those regions, the coastal circulation is constrained by a complex topography (e.g. varying width of the continental shelf, canyons), river runoffs, strong tides and a seasonally contrasted wind-driven circulation. Based on different numerical model experiments (from 400m to 4km spatial resolution, from 40 to 100 sigma vertical layers using 3D primitive equation ocean models), different features of the Bay of Biscay and English Channel circulation are assessed and explored. Both spatial (submesoscale and mesoscale) and temporal (from hourly to monthly) scales are considered. Modelled spatial scales, with a specific focus on the variability of fine scale features (e.g. fronts, filaments, eddies), are compared with remotely sensed observations (i.e. Sea Surface Temperature). Different methodologies as singularity and Lyapunov exponents allow describing fine scales features and are applied on both modelled and observed datasets. For temporal scales, in situ high frequency surface temperature measurements from coastal moorings (from COAST-HF observing network) provide a reference for the temporal variability to be modelled. Exploring differences in the temporal scales (from an Empirical Mode Decomposition) advises on the efficiency of our coastal modelling approach. This result overview in the Bay of Biscay and the English Channel aims illustrating the input of coastal modelling activities in understanding multi-scale interactions (spatial and temporal).

Context. Dense molecular filaments are ubiquituous in the interstellar medium, yet their internal physical conditions and formation mechanism remain debated. Aims. We study the kinematics and physical conditions in the Musca filament and the Chamaeleon-Musca complex to constrain the physics of filament formation. Methods. We produced CO(2-1) isotopologue maps with the APEX telescope that cut through the Musca filament. We further study a NANTEN2 $^{12}$CO(1-0) map of the Musca cloud and the HI emission of the Chamaeleon-Musca complex. Results. The Musca cloud contains multiple velocity components. Radiative transfer modelling of the CO emission indicates that the Musca filament consists of a cold ($\sim$10 K), dense (n$_{H_2}\sim$10$^4$ cm$^{-3}$) crest, which is best described with a cylindrical geometry. Connected to the crest, a separate gas component at T$\sim$15 K and n$_{H_2}\sim$10$^3$ cm$^{-3}$ is found, the so-called strands. The filament crest has a transverse velocity gradient that is linked to the kinematics of the nearby ambient cloud. Studying the large scale kinematics, we find coherence of the asymmetric kinematics from the 50 pc HI cloud down to the Musca filament. We also report a strong [C$^{18}$O]/[$^{13}$CO] abundance drop by an order of magnitude from the filament crest to the strands over a distance $, Comment: 24 pages, 26 figures

International audience; Analysis and study of coastal upwelling using sea surface temperature (SST) satellite images is a common procedure because of its coast effectiveness (economic, time, frequency, and manpower). Developing on the Ekman theory, we propose a robust method to identify the upwelling regions along the northwest African margin. The proposed method comes to overcome the issues encountered in a recent method devoted for the same purpose and for the same upwelling system. Afterward, we show how our method can serve as a framework to study and monitor the spatio-temporal variability of the upwelling phenomenon in the studied region.

A new methodology has been developed in order to improve the description of the spatial and temporal variability of not well-resolved oceanic variables from other well-observed high-resolution oceanic variables. The method is based on the cross-scale inference of information, incorporating the common features of different multifractal high-resolution variables into a coarser one. An exercise of validation of the methodology has been performed based on the outputs of coupled physical-biogeochemical Regional Ocean Modeling System adapted to the eastern boundary upwelling systems at two spatial resolutions. Once the algorithm has been proved to be effective in increasing the spatial resolution of modeled partial pressure of CO2 at the surface ocean (pCO2), we have investigated the capability of our methodology when it is applied to remote sensing data, focusing on the improvement of the temporal description. In this regard, we have inferred daily pCO2 maps at high resolution (4 km, i.e., 1/24°) fusing monthly pCO2 data at low resolution (100 km, i.e., 1°) with the small-scale features contained in daily high-resolution maps of satellite sea surface temperature and Chlorophyll-a. The algorithm has been applied to the South Eastern Atlantic Ocean opening the possibility to obtain an accurate quantification of the CO2 fluxes in relevant coastal regions, such as the eastern boundary upwelling systems. Outputs of our algorithm have been compared with in situ measurements, showing that daily maps inferred from monthly products are in average 6 $\mu $ atm closer to the in situ values than the original coarser monthly maps. Furthermore, values of pCO2 have been improved in points close to the coast with respect to the original input data.

International audience; An efficient unsupervised method is developed for automatic segmentation of the area covered by upwelling waters in the coastal ocean of Morocco using the Sea Surface Temperature (SST) satellite images. The proposed approach first uses the two popular unsupervised clustering techniques, k-means and fuzzy c-means (FCM), to provide different possible classifications to each SST image. Then several cluster validity indices are combined in order to determine the optimal number of clusters, followed by a cluster fusion scheme, which merges consecutive clusters to produce a first segmentation of upwelling area. The region-growing algorithm is then used to filter noisy residuals and to extract the final upwelling region. The performance of our algorithm is compared to a popular algorithm used to detect upwelling regions and is validated by an oceanographer over a database of 92 SST images covering each week of the years 2006 and 2007. The results show that our proposed method outperforms the latter algorithm, in terms of segmentation accuracy and computational efficiency.

International audience; The frontal activity in coastal regions remains a research field where a large number of open questions needs to be addressed to quantify the potential impact of these processes on dependent systems (e.g.biogeochemical activity). Spatial and seasonal distributions of Sea Surface Temperature (SST) fronts (∼1-100 km) in the vicinity of main French rivers, Gironde and Loire, are explored over the continental shelf North of 45°N in the Bay of Biscay. A highresolution (1km spatial and daily temporal resolutions) dataset of 11 years (2003 to 2013) remotely sensed SST by MODIS sensor onboard Aqua and Terra satellites has been investigated and compared with coastal numerical model experiments. The detection and characterization fronts with fluctuating amplitudes is achieved through the Singularity Analysis (i.e. the process of calculating the degree of regularity or irregularity of a function at each point in a domain). Seasonality of frontal activity in the Bay of Biscay is then described based on the long-term satellite SST archive and coastal operational model simulations. The identifiedhot spots of higher frontal occurrences correspond on one hand to previously observed features (e.g. tidal fronts) but also reveal new features. These are investigated to identify fine-scale dynamical drivers. In winter, density fronts are prominent in a coastal strip where freshwater influence is important. In spring, this strip diminishes as plumes detach from the coast, while tidal fronts become apparent in other regions. In summer, tidal fronts in Ushant region and internal wave activity along the shelf break dominate. In autumn, coastal density fronts due to freshwater inputs reappear as these inputs increase, and reduced stratification causes a weakening of the Ushant and shelf break fronts.Additional information and an effort to dynamically interpret these fronts, based on a systematic investigation of the whole seasonal cycle and including modeling insights from coastal operational oceanography, complement the description of frontal activity in the Bay of Biscay.

International audience; We study the transport properties of coherent vortices over a finite time duration. Here we reveal that such vortices can be identified based on frequency-domain representation of Lagrangian trajectories. We use Fourier analysis to convert particles' trajectories from their time domain to a presentation in the frequency domain. We then identify and extract coherent vortices as material surfaces along which particles' trajectories share similar frequencies. Our method identifies all coherent vortices in an automatic manner, showing high vortices' monitoring capacity. We illustrate our new method by identifying and extracting Lagrangian coherent vortices in different two-and three-dimensional flows.

Normalized Euclidean distance (NED) and normalized signature correlation mapper (NSCM) are most popularly used pattern classifiers with polarization signatures (PSs) based polarimetric synthetic aperture radar (PolSAR) data applications. These methods are not able to fully exploit the PSs as they do not exploit the spatial context or pattern of PSs which is essential. Improved utilization of PSs is still required for PolSAR applications such as agriculture crop classification and monitoring. In this study, convolutional deep neural networks (C-DNNs) are introduced and utilized as pattern classifiers for PS classification. C-DNNs have the ability to consider and control the influence of local neighborhood pixels during classification. Therefore, in this study C-DNNs are utilized to extract and exploit subtle changes between PSs of land covers to improve classification performance. Comparison with NED and NSCM classifiers signify the contribution of C-DNNs by improved performance in PolSAR data classification.

The advent of Fluorescence Microscopy over the last few years have dramatically improved the problem of visualization and tracking of specific cellular objects for biological inference. But like any other imaging system, fluorescence microscopy has its own limitations. The resultant images suffer from the effect of noise due to both signal dependent and signal independent factors, thereby limiting the possibility of biological inferencing. Denoising is a class of image processing algorithms that aim to remove noise from acquired images and has gained wide attention in the field of fluorescence microscopy image restoration. In this paper, we propose an image denoising algorithm based on the concept of feature extraction through multifractal decomposition and then estimate a noise free image from the gradients restricted to these features. Experimental results over simulated and real fluorescence microscopy data prove the merit of the proposed approach, both visually and quantitatively.

International audience; We propose a new approach to enforce integrability using recent advances in non-local methods. Our formulation consists in a sparse gradient data-fitting term to handle outliers together with a gradient-domain non-local low-rank prior. This regularization has two main advantages : 1) the low-rank prior ensures similarity between non-local gradient patches, which helps recovering high-quality clean patches from severe outliers corruption, 2) the low-rank prior efficiently reduces dense noise as it has been shown in recent image restoration works. We propose an efficient solver for the resulting optimization formulation using alternate minimization. Experiments show that the new method leads to an important improvement compared to previous optimization methods and is able to efficiently handle both outliers and dense noise mixed together.

International audience; We study the transport properties of mesoscale eddies (i.e. vortices of 100 ∼ 200 km in diameter) over a finite time duration. While these oceanic structures are well-known to stir and mix surrounding water, they can also carry and transport water properties in a coherent manner. In this paper, we are interested in dynamic transport properties of these coherent structures, despite their chaotic environment. Here, we reveal that such vortices can be identified based a simple decomposition of their Lagrangian trajectories. We identify and extract coherent vortices as material lines along which particles' trajectories share similar polar rotations. The proposed method identifies coherent vortices and their centers in automatic manner. We illustrate our new method by identifying and extracting Lagrangian coherent vortices in different two-dimensional flows.

In this paper we aim to present a new methodology to derive rigorous SST-based coastal upwelling index for the purpose of conducting a saisonal variablity of upwelling area along the Moroccan Atlantic coast. The method is based on the scientific knowledge of upwelling area and its spatial distribution provided by expert oceanographers. The latter consists in automatically identify and extract the region covered by the upwelling waters in the costal ocean of Morocco using the Fuzzy c-means algorithm and finding regions of homogeneous pixels. Then Region Growing process is used to filter out the remaining noisy structures in the offshort waters. The methodology is used to provide a satistical view of the spatial and temporal variability of the Moroccan upwelling activity. The relevance of the proposed Coastal Upwelling Index (CUI) is evaluated by an oceanographer using 86 8-days sea surface temperature images and it is shown to be superior to that of the standard upwelling index.

International audience; We evidence and study the differences in turbulence statistics in ocean dynamics carried by wind forcing at the air-sea interface. Surface currents at the air-sea interaction are of crucial importance because they transport heat from low to high latitudes. At first order, oceanic currents are generated by the balance of the Coriolis and pressure gradient forces (geostrophic current) and the balance of the Coriolis and the frictional forces dominated by wind stress (Ekman current) in the surface ocean layers. The study was conducted by computing statistical moments on the shapes of spectra computed within the framework of microcanonical multi-fractal formalism. Remotely sensed daily datasets derived from one year of altimetry and wind data were used in this study, allowing for the computation of two kinds of vector fields: geostrophy with and geostrophy without wind stress forcing. We explore the statistical properties of singularity spectra computed from velocity norms and vorticity data, notably in relation with kurtosis information to underline the differences in the turbulent regimes associated with both kinds of velocity fields.

International audience; We study the transport properties of mesoscale eddies (i.e. vortices between about 10 and 500 km in diameter) over a finite time duration. While these oceanic structures are well known to stir and mix surrounding water masses by their swirling motion, they can also carry and transport organic matter and marine in a coherent manner. Here we are particularly interested in those that remain coherent, despite the chaotic nature of the system. Existing techniques to analyze such Coherent Vortices rely on the classical geometric theory of invariants of the Cauchy–Green strain tensor, where closed orbits of line fields associated with this later are defined as vortices. Others define coherent vortices from vorticity, by calculating its averaged deviation. In this work, we take a very different approach, based upon a rigorous analysis of particles trajectories. Our method identifies coherent vortices and their centers acting as observed attractors for light particles in an automatic manner. We illustrate our new method on ocean surface velocities derived from satellite altimetry.

An accurate quantification of the role of the ocean as source/sink of greenhouse gases (GHGs) requires to access the high-resolution of the GHG air–sea flux at the interface. In this paper we present a novel method to reconstruct maps of surface ocean partial pressure of CO2 ( pCO2) and air–sea CO2 fluxes at super resolution (4 km, i.e., 1/32° at these latitudes) using sea surface temperature (SST) and ocean color (OC) data at this resolution, and CarbonTracker CO2 fluxes data at low resolution (110 km). Inference of super-resolution pCO2 and air–sea CO2 fluxes is performed using novel nonlinear signal processing methodologies that prove efficient in the context of oceanography. The theoretical background comes from the microcanonical multifractal formalism which unlocks the geometrical determination of cascading properties of physical intensive variables. As a consequence, a multi-resolution analysis performed on the signal of the so-called singularity exponents allows for the correct and near optimal cross-scale inference of GHG fluxes, as the inference suits the geometric realization of the cascade. We apply such a methodology to the study offshore of the Benguela area. The inferred representation of oceanic partial pressure of CO2 improves and enhances the description provided by CarbonTracker, capturing the small-scale variability. We examine different combinations of ocean color and sea surface temperature products in order to increase the number of valid points and the quality of the inferred pCO2 field. The methodology is validated using in situ measurements by means of statistical errors. We find that mean absolute and relative errors in the inferred values of pCO2 with respect to in situ measurements are smaller than for CarbonTracker.

International audience; We present a new framework for fast edge-aware processing of images and videos. The proposed smoothing method is based on an optimization formulation with a non-convex sparse regularization for a better smoothing behavior near strong edges. We develop mathematical tools based on first order approximation of proximal operators to accelerate the proposed method while maintaining high-quality smoothing. The first order approximation is used to estimate a solution of the proximal form in a half-quadratic solver, and also to derive a warm-start solution that can be calculated quickly when the image is loaded by the user. We extend the method to large-scale processing by estimating the smoothing operation with independent 1D convolution operations. This approach linearly scales to the size of the image and can fully take advantage of parallel processing. The method supports full color filtering and turns out to be temporally coherent for fast video processing. We demonstrate the performance of the proposed method on various applications including image smoothing, detail manipulation, HDR tone-mapping, fast edge simplification and video edge-aware processing.

The neurological damage after cardiac arrest presents a huge challenge for hospital discharge. Therapeutic hypothermia (34 °C - 32 °C) has shown its benefits in reducing cerebral oxygen demand and improving neurological outcomes after cardiac arrest. However, it can have many adverse effects, among them cardiac arrhythmia generation which represents an important part (up to 34%, according different clinical studies). A monolayer cardiac culture is prepared with cardiomyocytes from a newborn rat, directly on a multi-electrode array, which allows the acquisition of the extracellular potential of the culture. The temperature range is 37 °C - 30 °C-37 °C, representing the cooling and rewarming process of therapeutic hypothermia. Experiments showed that at 35 °C, the acquired signals are characterized by period-doubling phenomenon, compared with signals at other temperatures. Spiral waves, commonly considered to be a sign of cardiac arrhythmia, are observed in the reconstructed activation map. With an approach from nonlinear dynamics, phase space reconstruction, it is shown that at 35 °C, the trajectories of these signals formed a spatial bifurcation, even trifurcation. Another transit point is found between 30 °C-33 °C, which agreed with other clinical studies that induced hypothermia after cardiac arrest should not fall below 32 °C. The process of therapeutic hypothermia after cardiac arrest can be represented by a pitchfork bifurcation type process, which could explain the different ratios of arrhythmia among the adverse effects after this therapy. This nonlinear dynamic suggests that a variable speed of cooling/rewarming, especially when passing 35 °C, would help to decrease the ratio of post-hypothermia arrhythmia and then improve the hospital output.

International audience; This paper presents a novel algorithm for automatic detection of Glottal Closure Instants (GCI) from the speech signal. Our approach is based on a novel multiscale method that relies on precise estimation of a multiscale parameter at each time instant in the signal domain. This parameter quantifies the degree of signal singularity at each sample from a multi-scale point of view and thus its value can be used to classify signal samples accordingly. We use this property to develop a simple algorithm for detection of GCIs and we show that for the case of clean speech, our algorithm performs almost as well as a recent state-of-the-art method. Next, by performing a comprehensive comparison in presence of 14 different types of noises, we show that our method is more accurate (particularly for very low SNRs). Our method has lower computational times compared to others and does not rely on an estimate of pitch period or any critical choice of parameters.

International audience; Current state-of-the-art denoising methods such as non-local low-rank approaches have shown to give impressive results. They are however mainly tuned to work with uniform Gaussian noise corruption and known variance, which is far from the real noise scenario. In fact, noise level estimation is already a challenging problem and denoising methods are quite sensitive to this parameter. Moreover, these methods are based on shrinkage models that are too simple to reflect reality, which results in over-smoothing of important structures such as small-scale text and textures. We propose in this paper a new approach for more realistic image restoration based on the concept of low-rankness transfer (LRT). Given a training clean/noisy image pair, our method learns a mapping between the non-local noisy singular values and the optimal values for denoising to be transfered to a new noisy input. One single image is enough for training the model and can be adapted to the noisy input by taking a correlated image. Experiments conducted on synthetic and real camera noise show that the proposed method leads to an important improvement both visually and in terms of PSNR/SSIM.

In this article, various notions of edges encountered in digital image processing are reviewed in terms of compact representation (or completion). We show that critical exponents defined in Statistical Physics lead to a much more coherent definition of edges, consistent across the scales in acquisitions of natural phenomena, such as high resolution natural images or turbulent acquisitions. Edges belong to the multiscale hierarchy of an underlying dynamics, they are understood from a statistical perspective well adapted to fit the case of natural images. Numerical computation methods for the evaluation of critical exponents in the non-ergodic case are recalled, which apply for the vast majority of natural images. We study the framework of reconstructible systems in a microcanonical formulation, show how it redefines edge completion, and how it can be used to evaluate and assess quantitatively the adequation of edges as candidates for compact representations. We study with particular attention the case of turbulent data, in which edges in the classical sense are particularly challenged. Tests are conducted and evaluated on a standard database for natural images. We test the newly introduced compact representation as an ideal candidate for evaluating turbulent cascading properties of complex images, and we show better reconstruction performance than the classical tested methods. HighlightsClassical edge detection algorithms are reviewed in terms of completion.A new edge detection scheme based on multiscale hierarchical classification.Assessing the consistency of edges across scales.Evaluating edge performance through the framework of reconstructible systems.Comparing the performance with classical edge operators.

International audience; Near-shore water along the NorthWest African margin is one of the world's major upwelling regions. It is associated with physical structures of oceanic fronts which influence the biological productivity. The study of these coherent structures in connection with chlorophyll concentration data is of fundamental importance for understanding the spatial distributions of the plankton. In this work, we study the horizontal stirring and mixing in different upwelling areas using Lagrangian coherent structures (LCSs). These LCSs are calculated using the recent geodesic theory of LCSs. We use these LCSs to study the link between the chlorophyll fronts concentrations and surface mixing, based on 10 years of satellite data. These LCSs move with the flow as material lines, thus the horizontal mixing is calculated from the intersection of these LCSs with the finite time Lyapunov exponents (FTLEs) maps. We compare our results with those of a recent study conducted over the same area, but based on Finite Size Lyapunov Exponents (FSLEs) whose output is a plot of scalar distributions. We discuss the differences between FSLE and geodesic theory of LCS. The latter yields analytical solutions of LCSs, while FSLEs can only provide LCSs for sharp enough ridges of nearly constant height.

International audience; In this paper, we show that a new edge detection scheme developed from the notion of transition in nonlinear physics, associated with the precise computation of its quantitative parameters (most notably singularity exponents) provide enhanced performances in terms of reconstruction of the whole image from its edge representation; moreover it is naturally robust to noise. The study of biological vision in mammals state the fact that major information in an image is encoded in its edges, the idea further supported by neurophysics. The first conclusion that can be drawn from this stated fact is that of being able to reconstruct accurately an image from the compact representation of its edge pixels. The paper focuses on how the idea of edge completion can be assessed quantitatively from the framework of reconstructible systems when evaluated in a microcanonical formulation; and how it redefines the adequation of edge as candidates for compact representation. In the process of doing so, we also propose an algorithm for image reconstruction from its edge feature and show that this new algorithm outperforms the well-known 'state-of-the-art' techniques, in terms of compact representation, in majority of the cases.

International audience; Presently, there is a need to explore the possibility to maximize the use of MODIS (Moderate Resolution Imaging Spectroradiometer) data as it has very good spectral (36 bands) and temporal resolution whereas its spatial resolution is moderate i.e. 250m, 500m, and 1km. Because of its moderate spatial resolution, its application for land cover classification is limited. Therefore, in this paper, an attempt has been made to enhance its spatial resolution and utilize the information contained in the different bands together to achieve good land cover classification accuracy, so that, in future, MODIS data can be used more effectively. For resolution enhancement, modified dual tree complex wavelet transform (DT-CWT) has been employed, where DT-CWT has been modified by critically analyzing the effect of weight factor of the DT-CWT coefficients on land cover classification. For this purpose, image statistics parameter like Mean of the image has also been considered. The proposed technique has been applied on the six bands of MODIS data which have spatial resolution of 500m. It is observed that weight factor of the high-frequency sub-bands is quite sensitive for computation of classification accuracy. Index Terms— DT-CWT, Resolution enhancement, wavelets, weights, MODIS 1.INTRODUCTION Satellite images are being used in various applications such as geoscience studies, astronomy and geographical information systems where their resolution plays a critical role but on the other hand, directly obtaining a high resolution data is an another herculean task because of high cost of sensor. Land cover classification from satellite data is a central topic in satellite imaging applications. Therefore, it becomes a necessity to develop and utilize a reliable resolution enhancement technique to obtain accurate information as much as possible as per application from the freely available moderate resolution satellite data. In this regard, many image resolution enhancement techniques have been developed which are interpolations (nearest neighbor, bilinear and bicubic) and wavelets (DWT, SWT, WZP etc.) based. Interpolation techniques [1] have been widely used for resolution enhancement but it results in loss of edges (i.e., high frequency components) of an image. Nowadays, resolution enhancement is being carried out in the wavelet domain. There are many wavelet transforms which have acquired the place. Discrete wavelet transform (DWT) [2] has also been widely used in order to preserve the high-frequency components of the image but its disadvantage is that it ends up with some ringing artifacts into the image since it is not found to be shift-invariant because of decimations and suppression of wavelet coefficients exploited by DWT. It basically suffers from four shortcomings i.e., oscillations, shift variance, aliasing and lack of directionality which can lead to some artifacts in the image and difficulties in signal modeling. Hence, the DWT has somewhat disappointed the researchers for satellite images. Therefore, in order to alleviate all these drawbacks of DWT [2,3], a new kind of wavelet was introduced by Kingsbury which is known as DT-CWT (Dual tree complex wavelet transform) [1,3]. It possesses shift-invariant property and has the capability of improving directional resolution (because of good directional sensitivity) as compared to that of the decimated DWT. That's why, DT-CWT has been employed in this paper for resolution enhancement of moderate resolution satellite images. It is foremost to discover the possibility of maximizing the use of freely available satellite data like MODIS. It consists of several bands in which different information is present, but has certain limitations as well like low spatial resolution i.e. 500m which is a major obstacle in obtaining that information accurately. Many researchers have worked on resolution enhancement techniques for visualization enhancement whereas in this paper, main motive is to enhance the land cover classification accuracy which is not reported much for satellite images like MODIS yet. Variance minimization [4] has also been explored by several researchers for weights optimization but it is somewhat 4528 978-1-5090-3332-4/16/$31.00

International audience; Data acquired from the electrical activity of human hearts during episodes of atrial fibrillation, a disordered arrhythmia that is a major cause of stroke, reveals intriguing features for an excitable media: highly skew symmetric probability dis- tributions with heavy tails, long range correlations, and broad singularity spectra. Interestingly, the relevant exponents extracted from these empirical laws are stable over several minutes but not universal. Their stable values are distributed among pa- tients and areas of the heart. The question of central clinical purpose is whether they might characterize locally the myocardium contingent pathology. To achieve clarifi- cation of these peculiar facts, we were led to devise a phenomenological model that departs from the conventional approach to fibrillation. Instead of a defect mediated spiral wave “turbulence” induced by front collisions, fibrillation is pictured here as a highly intermittent modulation of cardiac pulse trains. It is based on the physiology of inter-cellular ionic exchanges, which is associated with the natural degree of free- dom of the inter-pulse duration. We infer an experimentally unknown slow dynamics of inter-cellular coupling, that may induce an inter-pulse effective coupling. This in- teraction creates a modulation that may lead to intermittency in various ways. The exchange of charges occurs at small scales in the model. They are passively advected at each interstitial junction on fast time scales and on average collectively driving the larger scales. In fact, a dimensionless number characterizing the dynamics is an analogue of the Rayleigh number. Away from a rapidly beating source, random back scattering makes pulses follow random hierarchical “percolating” paths in 1D. We dis- cuss very briefly the topological origin of these dynamics. In the light of this model, we don’t omit to mention some important physiological aspects of the pathology that are still not well understood and more possibilities for the case which comes to grip with sudden cardiac death.

17 pages, 7 figures, In this article it is shown that the multifractal microcanonical formalism (herein referred to as MMF) has strong potential for bringing new solutions to a known problem in the analysis of some remotely sensed datasets: the determination of fire plumes in NOAA-AVHRR data. It has been proven that NOAA-AVHRR data can be used to detect plumes caused by fire accidents of different kinds. This work builds on previous studies and uses the MMF to introduce novel methods for the determination of plumes. The MMF can be used to derive geometrical superstructures (like certain multifractal topological manifolds and most importantly the so-called reduced signals) that are able to deal with the multiscale properties of turbulent geophysical fluid flows. These multiscale properties make use of the spatial distribution of grey-level values in the datasets and they are used in conjunction with previous pixel-based descriptors to enhance the determination of plume pixels, This work was done in the framework of the PLUMESAT project (‘Use of a satellite ground receiving station for the detection and monitoring of fires and plumes caused by major natural and man-made disasters’). The PLUMESAT project is funded by: (i) The ‘Competitveness’ Programme, Action 4.3.6.1.γ of the General Secretary of Research and Technology of the Ministry of Development of Greece, and (ii) The ‘Platon 2005’ Integrated Action Programme (PAI) of the French Ministry of Foreign Affairs, through EGIDE

International audience; The aim of this work is to automatically identify and extract the upwelling area in the coastal ocean of Morocco using the satellite observation of chlorophyll concentration. The algorithm starts by the application of FCM algorithm for the purpose of finding regions of homogeneous concentration of the chlorophyll, resulting in c-partitioned labeled images. A region­ growing algorithm is then used to filter out the noisy structures in the offshore waters not belonging to the upwelling regions. The proposed methodology has been validated by an oceanographer and tested over a database of 166 weekly Sea Surface chlorophyll data. The region of interst cover the southern part of Moroccan atlantic coast spanning from the years 2007 to 2012.

International audience; Turbulence in the Earth's atmosphere interferes with the propagation of planar wavefronts from outer space resulting in a phase distorted non-planar wavefront. This phase distortion is responsible for the refractive blurring of images accounting to the loss in spatial resolution power of ground-based telescopes. The technology widely used to remove this phase distortion is Adaptive Optics (AO). In AO, an estimate of the distorted phase is provided by a wavefront sensor (WFS) in the form of low-resolution slope measurements of the wavefront. The estimate is then used to create a corrected wavefront, that (approximately) removes the phase distortion from the incoming wavefronts. Phase reconstruction from WFS measurements is done by solving large linear systems followed by interpolating the low-resolution phase to its desired high-resolution. In this paper, we propose an alternate technique to wavefront phase reconstruction using concepts derived from the Microcanonical Multiscale Formalism (MMF), which is a specific approach to multifractality. We take into account an a priori information of the wavefront phase, provided by the multifractal exponents. Then through the framework of multiresolution analysis and wavelet transform, we address the problem of phase reconstruction from low-resolution WFS measurements. Comparison, in terms of reconstruction quality, with classical techniques in AO proves the superiority of our approach.

International audience; Background: The animal models (in vitro or in vivo) provide an excel-lent tool to study heart diseases, among them the arrhythmia remains one of the most active research subjects. It can be induced or treated by drugs, electrical stimulation, hypothermia etc.Problems: However, the inducing or treating effects in cardiac culture often happened long after the initial applications or in some relatively short time windows. So, it is necessary to capture and classify rapidly the signal change. Human-assisted monitoring is time-consuming and less efficient. An automatic classification method for real-time use would be useful and necessary. Methods: Since electrocardiological signals are features by repetitive or similar patterns reflecting the intrinsic information about the pa-tient (or culture), analyzing these patterns could help not only to monitor the status's change but also to evaluate/explore the physiol-ogic control mechanisms. Methods based on complexity analysis are of considerable interest in this case. Aims: Compare different complexity analysis methods in order to find the most appropriate ones to discriminate the normal cardiac signals from arrhythmic ones acquired from a cardiac cell culture in vitro. The selected features are then used by a SVM classifier.Results: Among the six complexity analysis methods, Time Lagging (TLag) method allowed obtaining the best discrimination index (nor-mal vs. arrhythmic, p-value, 9e-23). The proposed Modified Hurst Exponent (HExM) showed better performance than original Hurst Exponent with well-improved p-value (from 0.019 to 2e-9). The Ap-proximate Entropy (ApEn), Sample Entropy (SampEn) and Detrended Fluctuation Analysis gave good discrimination ratio but with larger p-values (at order 10^{-3}). Combination of TLag, HExM and ApEn can provide a more robust classifier and allow monitoring and classifying in an automatic way the electrical activities' changes in the cardiac cultures.

International audience; The synoptic determination of ocean circulation using the data acquired from space, with a coherent depiction of its turbulent characteristics, remains a fundamental challenge in oceanography. This determination has the potential of revealing all aspects of the ocean dynamic variability on a wide range of spatiotemporal scales and will enhance our understanding of ocean–atmosphere exchanges at superresolution, as required in the present context of climate change. Here, we show a four-year time series of spatial superresolution (4 km) turbulent ocean dynamics generated from satellite data using emerging ideas in signal processing coming from nonlinear physics, low-resolution dynamics, and superresolution oceanic sea surface temperature data acquired from optical sensors. The method at its core consists in propagating across the scales the low-resolution dynamics in a multiresolution analysis computed on adimensional critical transition information.

Algorithm for detection of thermal upwelling fronts in SST images is presented.The algorithm makes use of the singularity exponents.The singularity exponents are computed in a microcanonical framework.Performance of the algorithm is compared to an automatic algorithm for satellite edge detection.The algorithm is applied and validated by an oceanographer over 92 SST images. Nonlinear signal processing using the Microcanonical Multiscale Formalism (MMF) is used to the problem of detecting and extracting the upwelling fronts in coastal region of Morocco using Sea Surface Temperature (SST) satellite images. The algorithm makes use of the Singularity Exponents (SE), computed in a microcanonical framework, to detect and analyze the critical transitions in oceanographic satellite data. The objective of the proposed study is to develop a helpful preprocessor that transforms SST images into clean and simple line drawing of upwelling fronts as an input to a subsequent step in the analysis of SST images of the ocean. The method is validated by an oceanographer and it is shown to be superior to that of an automatic algorithm commonly used to locate edges in satellite oceanographic images. The proposed approach is applied over a collection of 92 SST images, covering the southern Moroccan Atlantic coast of the years 2006 and 2007. The results indicate that the approach is promising and reliable for a wide variety of oceanographic conditions.

Satellite image resolution enhancement is an active area of research. There are many techniques which perform well in the Standard image domain but tend to give inconsistent and unacceptable results in the case of satellite images. Thus there is a need to modify the existing techniques and develop new ones to overcome this short coming. This paper presents several image resolution enhancement techniques like WZP (Wavelet Zero Padding), WZP-CS (Wavelet Zero padding and Cycle Spinning), DWT (Discrete Wavelet Transform) and DWT-SWT with Error back projection that were adapted to the satellite images by modifications to the original technique. These techniques were applied on MODIS (Moderate Resolution Imaging Spectroradiometer) images and the results have been evaluated using standard quality assessment parameters like PSNR, RMSE and Correlation Coefficient. The initial moderate resolution and the final high resolution images obtained through the different techniques are also illustrated in the paper. The outcome of this evaluation indicates that the techniques which achieved very good PSNR values in Standard images do not necessarily reiterate the same in case of satellite images. The results also reinforce that the modified techniques fare well to preserve the information content of the satellite images and improve the quality.

International audience; This paper presents the application of a radically novel approach, called the Microcanonical Multiscale Formalism (MMF) to speech analysis. MMF is based on precise estimation of local scaling parameters that describe the inter-scale correlations at each point in the signal domain and provides e cient means for studying local non-linear dynamics of complex signals. In this paper we introduce an e cient way for estimation of these parameters and then, we show that they convey relevant information about local dynamics of the speech signal that can be used for the task of phonetic segmentation. We thus develop a two-stage segmentation algorithm: for the first step, we introduce a new dynamic programming technique to e ciently generate an initial list of phoneme-boundary candidates and in the second step, we use hypothesis testing to refine the initial list of candidates. We present extensive experiments on the full TIMIT database. The results show that our algorithm is significantly more accurate than state-of-the-art ones.

International audience; An unsupervised classification method is developed for the coarse segmentation of Moroccan coastal upwelling using the Sea Surface Temperature (SST) satellite images. The algorithm is started with the generation of c-partitioned labeled image using Otsu's method for the purpose of finding regions of homogenous temperatures. Then two well-known validity indices are used to select the c-partition which best reproduce the shape of upwelling area. A region-growing algorithm is developed that is used to remove the noisy structures in the offshore waters not belonging to the upwelling area. The algorithm is used to provide a seasonal variability of upwelling activity in the southern Moroccan Atlantic coast using 70 SST images of the years 2007 and 2008. The performance of the proposed methodology has been validated by an oceanographer, showing its effectiveness for automatic delimitation of Moroccan upwelling region.

Satellite images play a major role in the analysis of land cover, topographic analysis, geosciences etc. There has always existed a tradeoff between the image resolution and the image cost. In this paper, a modified discrete wavelet transform and interpolation based technique is proposed for enhancing the resolution of satellite images having low resolution in such a way that a highly resolved satellite image can be obtained without losing any image information. The advent of DWT has given a major impetus to many techniques based on achieving super resolution starting with a single low resolution image. In the proposed method, DWT is employed on the input satellite image to decompose it into sub-bands then the high frequency sub-bands and the input low resolution satellite image have been interpolated to obtain four interpolated images which are later combined after minor alterations to the interpolated input image using IDWT. The quantitative peak signal-to-noise ratio (PSNR) and classification results show that the resolution has been enhanced to a good scale without losing any information content present in the satellite image. The quality assessment parameters also illustrate the supremacy of the proposed technique over the conventional techniques.

4 pages, 4 figures.-- © 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, It is thought that satellite thermal infrared (IR) images can aid to the detection of precipitation, an interesting possibility due to the existence of geostationary satellites with thermal IR sensors which would enable a good spatial and temporal tracking of rain and storms. In this letter, we explore the application of multiscale/multifractal techniques in the design of new methods for the assessment and tracking of pluviometry. We first identify the main streamlines by a singularity analysis of the wavelet projections of the IR record. From the streamlines, we derive a proxy scalar image that represents the result of pure horizontal advection. From the comparison of original and proxy we localize the places at which horizontal advection fails, which we identify with convection places. We illustrate our methodology with thermal IR images from Metosat acquired during heavy tropical rainfall, and compare the results with some data from the Tropical Rainfall Measuring Mission satellite. © 2005 IEEE

International audience; Non-blind deconvolution consists in recovering a sharp latent image from a blurred image with a known kernel. Decon-volved images usually contain unpleasant artifacts due to the ill-posedness of the problem even when the kernel is known. Making use of natural sparse priors has shown to reduce ring-ing artifacts but handling noise remains limited. On the other hand, non-local priors have shown to give the best results in image denoising. We propose in this paper to combine both local and non-local priors to handle noise. We show that the blur increases the self-similarity within an image and thus makes non-local priors a good choice for denoising blurred images. However, denoising introduces outliers which are not Gaussian and should be well modeled. Experiments show that our method produces a better image reconstruction both visually and empirically compared to methods some popular methods.

International audience; Derived from the pioneer ionic Hodgkin-Huxley model and due to its simplicity and richness from a point view of nonlinear dynamics, the FitzHugh-Nagumo model has been one of the most successful neuron / cardiac cell model. It exists many variations of the original FHN model. Though these FHN type models help to enrich the dynamics of the FHN model. The parameters used in these models are often in biased conditions. The related results would be questionable. So, in this study, the aim is to find the parameter thresholds for one of the commonly used FHN model in order to pride a better simulation environment. The results showed at first that inappropriate time step and integration tolerance in numerical solution of FHN model can give some biased results which would make some publications questionable. Then the thresholds of parameters $\alpha$, $\gamma$ and $\varepsilon$ are presented. $\alpha$ controls the global dynamics of FHN. $\alpha > 0$, the cell is in refractory mode; $\alpha < 0$, the cell is excitable. $\varepsilon$ controls the main morphology of the action potential generated and has a relation with the period ($\mathrm{P} = 3.065 \times \varepsilon^{-0.8275}+4.397$). To show oscillations of relaxation with FHN, $\varepsilon$ should be smaller than $0.0085$. $\gamma$ influences barely AP, it showed linear relationship with the period and duration of action potential. Globally, when $|\alpha| \geq 0.1$, $\varepsilon < 0.0085$, there is no definite threshold for $\gamma$, but smaller values are recommended.

International audience; Reconstructing a surface/image from corrupted gradient fields is a crucial step in many imaging applications where a gradient field is subject to both noise and unlocalized outliers, resulting typically in a non-integrable field. We present in this paper a new optimization method for robust surface reconstruction. The proposed formulation is based on a triple sparsity prior : a sparse prior on the residual gradient field and a double sparse prior on the surface it- self. We develop an efficient alternate minimization strategy to solve the proposed optimization problem. The method is able to recover a good quality surface from severely cor- rupted gradients thanks to its ability to handle both noise and outliers. We demonstrate the performance of the pro- posed method on synthetic and real data. Experiments show that the proposed solution outperforms some existing meth- ods in the three possible cases : noise only, outliers only and mixed noise/outliers.

International audience; We introduce a new approach to wavefront phase reconstruction in Adaptive Optics using a non-linear approach derived from the Microcanonical Multiscale Formalism (MMF). MMF comes from established concepts in statistical physics, well-suited for the multiscale analysis of complex signals through the precise numerical estimate of geometrically localized singularity exponents. These exponents quantify the degree of predictability at each point of the signal domain, and provide information on the dynamics of the associated system. We show that multiresolution analysis on the singularity exponents of a high-resolution turbulent phase allows propagation along the scales of the phase gradients in low-resolution, to a higher resolution and offers an innovative approach to wavefront phase reconstruction in Adaptive Optics.