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 north-west 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 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.

In this letter, we propose a speckle removal denoising 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.

The region along the North-West African coast (20°N to 36°N and 4°W to 19°W) is characterized by a persistent and variable upwelling phenomenon almost all year round. In this article, the upwelling features are investigated using an algorithm dedicated to delimit the upwelling area from thermal and biological satellite observations. This method has been developed specifically for sea-surface temperature (SST) images, since they present a high latitudinal variation, which is not present in chlorophyll-a concentration images. Developing on the proposed approach, the spatial and temporal variations of the main physical and biological upwelling patterns are studied. Moreover, a study on the upwelling dynamics, which explores the interplay between the upwelling spatiotemporal extents and intensity, is presented, based on a 14-year time archive of weekly SST and chlorophyll-a concentration data.

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.

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.

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 CO2at 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 pCO2maps at high resolution (4 km, i.e., 1/24°) fusing monthly pCO2data 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 CO2fluxes 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 μatm closer to the in situ values than the original coarser monthly maps. Furthermore, values of pCO2have been improved in points close to the coast with respect to the original input data.

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.

Turbulence in the Earth's atmosphere interferes with the propagation of planar wavefronts from outer space, resulting in a phase-distorted nonplanar 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, 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.

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 and 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 with previous optimization methods and is able to efficiently handle both outliers and dense noise mixed together.

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. 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.

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.

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 a 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 that best reproduces 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 the Moroccan upwelling region.

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.

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.

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 gradients. 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 corrupted gradients thanks to its ability to handle both noise and outliers. We demonstrate the performance of the proposed method on synthetic and real data. Experiments show that the proposed solution outperforms some existing methods in the three possible cases: noise only, outliers only and mixed noise/outliers.

Non-blind deconvolution consists in recovering a sharp latent image from a blurred image with a known kernel. Deconvolved 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 ringing 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.

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.

The EBUS (Eastern Boundary Upwelling Systems) and OMZs (Oxygen Minimum Zone) contribute very significantly to the gas exchange between the ocean and the atmosphere, notably with respect to the greenhouse gases (hereafter GHG). From in-situ ocean measurements, the uncertainty of the net global ocean-atmosphere CO2fluxes is between 20 and 30%, and could be much higher in the EBUS-OMZ. Off Peru, very few in-situ data are available presently, which justifies alternative approaches for assessing these fluxes. In this contribution we introduce.

In an earlier work, we proposed a novel phonetic segmentation method based on speech analysis under the Microcanonical Multiscale Formalism (MMF). The latter relies on the computation of local geometrical parameters, singularity exponents (SE). We showed that SE convey valuable information about the local dynamics of speech that can readily and simply used to detect phoneme boundaries. By performing error analysis of our original algorithm, in this paper we propose a 2-steps technique which better exploits SE to improve the segmentation accuracy. In the first step, we detect the boundaries of the original signal and of a low-pass filtred version, and we consider the union of all detected boundaries as candidates. In the second step, we use a hypothesis test over the local SE distribution of the original signal to select the final boundaries. We carry out a detailed evaluation and comparison over the full training set of the TIMIT database which could be useful to other researchers for comparison purposes. The results show that the new algorithm not only outperforms the original one, but also is significantly much more accurate than state-of-the-art ones.

International audience

In this study, we show how different spectral channels of NOAA-AVHRR acquired data can be used to produce a synthetized signal aimed at helping the characterization of plumes associated to fire events. The synthetized signal is computed using a reconstruction formula in the multifractal microcanonical formalism (herein referred to as MMF). The MMF is a recent development in the analysis of complex signals, well adapted to the study of turbulent acquired data, for instance geophysical fluids. It allows the computation, at each point of the signal's domain, of a singularity exponent, characteristic of the scale behaviour of the signal around that point; singularity exponents provide information about the strengths of the transitions inside a signal, and they are related to the multifractal hierarchy associated to structure functions in Fully Developped Turbulence (FDT). In the MMF, it is possible to reconstruct a turbulent signal from the manifold of most singular exponents. We make use of this property by computing supergeometric structures from a thermal infrared channel in NOAA-AVHRR acquired data, and we use the signal's gradient coming from other channels to reconstruct a signal in which plume pixels are easier to detect. This methodology is based on the turbulent properties of the plume accessible from the thermal infrared band; the algorithm is detailed and applied on a specific example, showing a new spatially-based method for helping the determination of plume pixels in NOAA-AVHRR data.

The last generation of satellites leads to the very high-resolution images which offer a high quality of detailed information about the Earth's surface. However, the exploitation of such images becomes more complicated and less efficient as a consequence of the great heterogeneity of the objects displayed. In this paper, we address the problem of edge-preserving smoothing of high-resolution satellite images. We introduce a novel approach as a preprocessing step for feature extraction and/or image segmentation. The method we propose is related with the idea of resolution reduction and is derived from the multifractal formalism used for image compression. First, a multifractal decomposition scheme allows to extract the most singular transitions of the image. Then, an entropy-based criterium enables to consider a particular manifold composed with the most, simultaneously, relevant and singular pixels. Finally, a reconstruction scheme performed over this manifold provides an approximation of the original image. Such an approach is ideal, as it assumes that objects can be reconstructed from their boundary information, and it provides presegmented images where the main structures are preserved.

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.

International audience

We are interested in the problem of modeling the propagation of landslides. We use an image processing approach based on the computation of a geodesic distance map. We determine the evolution of an initial curve along a given surface. The model can incorporate various properties coming from the geometry of the surface or independant measures. In an application, we focus on landslides study in the context of natural risk management The model is driven by geometrical constraints and SAR interferometric measures.

The general problem of surface matching is considered in this study. The process described in this work hinges on a geodesic distance equation for a family of surfaces embedded in the graph of a cost function. The cost function represents the geometrical matching criterion between the two 3D surfaces. This graph is a hypersurface in 4-dimensional space, and the theory presented herein is a generalization of the geodesic curve evolution method introduced by R. Kimmel et al. (1995). It also generalizes the 2D matching process developed in Cohen and Herlin (1998). An Eulerian level-set formulation of the geodesic surface evolution is also used, leading to a numerical scheme for solving partial differential equations originating from hyperbolic conservation laws, which has proven to be very robust and stable. The method is applied on examples showing both small and large deformations, and arbitrary topological changes.

Tackles the general problem of surface tracking. The authors present a new method based on the computation of geodesic distance maps between a source and a destination surface. The method used in this case is a generalization to the case of surfaces of a 2D matching process introduced by Cohen and Herlin (1998). It relies on a level-set formulation, thus providing stable and robust resolution schemes. The obtained matching method accommodates both small and large deformations. Actually, the matching constraints are explicitly expressed by the definition of a hypersurface. The connecting paths between the two surfaces lie on this hypersurface. The authors construct a hypersurface that allows the matching process to preserve geometric similarities in the case of small deformations, and loosen these geometric constraints when large deformations occur. The applicative framework is the study of the Earth's crustal evolution to characterize the deformation of digital elevation models.

In this study the problem of modeling a family of curves is addressed. The need of such modeling appears frequently in many aspects of image processing where many linear structures keep spatial relationships during their evolution. We present a modeling tool well suited to the spatial modeling of a family of curves, and which can be very useful for motion tracking and curve evolution as well. The family of curves is represented as the line paths (orbits) of a "spline vector field", i.e. a vector field interpolating data using a framework similar to the theory of spline curves. The model is exemplified with oceanic satellite data. Its usefulness for curve evolution modeling is also presented.

Level-sets provide powerful methods for the segmentation of deformable structures. They are able to handle protrusions and specific topological effects. A particle system formulation of level-sets is introduced. It keeps all the advantages of the level-set approach for the segmentation of deformable structures, while it overcomes some of its drawbacks. In this approach the level-sets are controlled by particles, which is of particular interest for interactive control. The particle system records the internal energy of the level-set, while the external force field comes from image data. The energy minimization process is fast, stable and robust. The use of skeleton techniques provide a reliable initialization of the particles, and it is coherent with simple affine motion. The paper is illustrated by examples coming from real image sequences.

A method for the segmentation of deformable structures using a formulation of level-sets with particles system is presented. This results in an original energetic formulation, and leads to a fast and robust method for computing the segmentation. Initialization is performed with the help of skeleton techniques, providing a stable and coherent initialization. It is possible to add or delete particles during the minimization process, and the use of particles systems makes it possible to contemplate hierarchical representations. The global shape is then represented by few control points, and more precision is obtained by locally adding new particles.

This work is devoted to segmentation and tracking of structures undergoing large deformation in an image sequence. The overall goal is the modeling of complex moving and deforming structures that simultaneously brings compression of data, segmentation and tracking on temporal sequences of images. Deforming entities are approximated by implicit templates, defined as level sets of some particular implicit functions. Implicit functions provide interesting tools for the modeling of visco-elastic objects. Structures are approximated using an energy minimization process; the energy functional carries information about position, tangent and curvature. The implicit functions are defined by some control points, their radius of influence, and an iso-value. Once the structures are segmented, tracking is modeled using the displacements of the control points and the variation of the radii of influence. The method is first applied on a sequence of images displaying a vortex formation, and the authors show that such a vortex structure can be segmented and tracked with implicit functions.

ISBN: 0-201-58889-7