Indigenous lands represent an efficient way to protect indigenous communities and environment in Brazil. However, these lands are also highly affected y the land use changes occuring in its surroundings. We quantified the land use changes in the Xingu basin based on MODIS EVI data between 2000 and 2006. We estimated the deforested area inside and outside the indigenous lands, the crop expansion and intensification around the protected areas. Our results indicate that, even if indigenous lands are efficient to limit deforestation (97.5% of deforestation is outside the indigenous lands), crop expansion and intensification (double crop systems) are increasing rapidly, what may imply pollution of headwaters of the Xingu river which crosses the protected area.

Apparent motion estimated on satellite data is used for example to compute the wind field in meteorology, and surface currents in oceanography. The satellite images display turbulent fluids with strong rotational patterns at different spatial and temporal scales. This specificity necessitates devising adapted methods, allowing to control the divergence and curl of the retrieved motion field. Vector spline methods are very adapted to that purpose. The vector spline problem is defined as finding a motion field that satisfies a temporal conservation equation at selected control points and that minimizes a regularity constraint in all the image domain. An exact solution of this problem can be found for the 2nd order div-curl regularity constraint. The retrieval of the solution does not require an iterative minimization procedure: a dense matrix must be inverted to compute the spline's coefficients. This matrix unfortunately becomes large and ill-conditioned as the number of control points increases, making the vector spline approach unsuitable for processing large satellite images. This paper presents a method called "Partition of Unity and Optical Flow" (PUOF), based on a decomposition of the spatial domain: local vector splines are computed in subdo-mains of the image, then merged using a partition of unity algorithm. The resulting motion field is a good approximation of the exact vector spline solution, and its retrieval is numerically stable and computationally affordable even when processing large data sets, as demonstrated by results obtained on sequences of synthetic and meteorological images.

We tackle the problem of coupling a geophysical simulation model with data coming from image processing. It needs to define the image observation space and to design an operator to transform results from the image space to the model space. In this study, we use a shallow-water oceanographic circulation model developed at MHI. We propose a processing chain first based on an image processing step relying on a dedicated motion estimation operator, and then a data assimilation step of the estimated velocity. We illustrate the method on different results without and with assimilation

This paper aims at estimating the apparent velocity from sequences of satellite images. This study is an illustration of a more general methodology for generating, from satellite images, pseudo-observations of physical variables, that are assimilated within a geophysical forecast model in view of improving the quality of its results. In the case of the presented study, the used satellite images are sequences of sea surface temperature (SST), from which pseudo-observations of sea surface velocities are generated, and assimilated within an ocean circulation model. The originality of the approach lies in the definition of an image model, that predicts the evolution of image information -here, SST- as a function of the pseudo-observations -here, surface velocity-. Satellite data are then assimilated within the image model, yielding an estimation of the pseudo-observations. In the case of this paper, this methods allows the estimation of sea surface velocities, even when large parts of the satellite images are corrupted by clouds. The image model plays the role of an intermediate model, between satellite data and the forecast model, and allows the assimilation of image information which is not directly linked to the state variables of the forecast model.

International audience

International audience

International audience

International audience

This paper addresses vegetation monitoring in European agricultural areas using Earth Observation satellites. Due to the small size of typical European fields, two complementary sensors are used, SPOT and NOAA-AVHRR, bringing the spatial and the temporal information respectively. A sub-pixel analysis of NOAA data using one SPOT image is performed to characterize fields with high spatial and temporal resolutions. To be used in an operational context, the method must have realistic data requirements. We define an operational scenario making use of only one SPOT image per site and a one year NOAA sequence, covering a large part of Europe. We first proceed to an unsupervised segmentation of the SPOT image; the NOAA data analysis on test sites provides the temporal evolution of vegetation; then, identification of fields is performed by minimizing a cost function measuring the similarity between the global reflectance observed on NOAA pixels and the reflectance computed from corresponding regions at SPOT resolution.

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.

Describes a fusion process between two different data sources, one providing an accurate spatial information, the other providing time series with a much coarser spatial scale. It is applied in the following remote sensing context: the forecast of cereals production, which is a challenging application of the new generation of Earth observation satellites. These two data types are required since agronomical models must be fed with a daily sampling of cereals reflectances, and since in Europe, fields have a relatively small size. SPOT-XS is wed to provide spatial information at the parcels level, a meso-scale sensor (here, NOAA-AVHRR), which outputs images of large areas every day, provides the temporal information. The combination of these two data sources makes it possible to daily estimate reflectances of main cultivations at the parcels level. The selected approach is as follows: a preliminary learning stage provides the reflectances of each type of cultivation; then operational scenarios are defined to apply the learning information in order to estimate statistics on large areas: using only one SPOT-XS image and meso-scale daily images, a fusion scheme makes it possible to obtain land use identification at high spatial resolution with its temporal behavior.

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.

Motion study in computer vision highly depends on the type of imagery and on the kind of the studied objects. A popular approach of non-rigid motion supposes that the objects are continuously and locally deformed. Local features, such as curvature extrema, are then often used. However, these features can not always be accurately computed, and motion may involve large deformation of the object between two consecutive temporal occurrences. In these cases, a real need exists for an approach that does not rely on local features. That study of motion requires additional information. We introduce a geometrical evolution model that enables one to generate a surface interpolating successive contours of the object during its temporal evolution. This geometrical model may be viewed as a simplification of a true physical model of motion. This approach is particularly well suited to remote sensed data: structures of interest do not have a well defined shape, and the temporal resolution may be poor, involving large deformation. The model is successfully applied to vortex tracking on sea color and meteorologic images.

Image analysis using texture and multifractal paradigms is addressed. Multifractal theory and its application to image description are discussed, and it is shown that this approach allows the discrete signal to be worked on directly. A system for texture classification that is based on a learning scheme and does not make use of any a priori model is introduced. Image segmentation is then considered, and the notion of mixed classes, which allows accurate detection of texture boundaries on complex images, is introduced.>