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International audience; The paper addresses land cover monitoring at large scale. It investigates an approach for extracting information from remote sensing data and evaluating the possibility of doing automated analysis to detect land use changes. A multi-resolution remote sensing data process is performed to obtain curves describing the temporal reflectance profiles related to land cover behaviour. Then a time-frequency transformation is used to extract relevant features characterizing these profiles. The features are then projected on a representation space improving discrimination between different land cover types. This approach can applied in a scenario dedicated to the detection of land use changes caused by erosion.

International audience; The European IWRMS (Integrated Water Resources Management System) project is dedicated to developing a toolset for a sustainable use and distribution of water resources in souther African countries. This system integrates various scientific components: remote sensing, information systems, database management, and hydrological modelling. This paper is mainly related to the remote sensing contribution of the project. Two points are discussed: land cover classification and the spatio-temporal processing of remote sensing data to extract hydrological parameters.

International audience; In this paper we present a framework to generate a land cover classification from coarse spatial resolution remotely sensed data acquired by NOAA-AVHRR sensor. We define a model for the pixels’ content and a process allowing to compute the individual proportions of the different land cover types for each pixel. The method is based on a linear mixture model of reflectances and exploits the good temporal frequency of NOAA acquisitions. The result provides a description in terms of land covers percentage within each NOAA pixel. A quality evaluation is performed on a test area for which high spatial resolution and temporal NOAA data are simultaneously available.

International audience; No abstract

In french; No abstract

Special Issue on Environmental Modelling, A. Sydow, Editor.; International audience; This presentation investigates the use of remote sensing image processing techniques to estimate input data for air quality models. These models actually use a lot of input data, whose collection can be inaccurate or costly. Thanks to their high spatial and temporal resolutions and their rich spectral content, remote sensing data can be analysed to estimate some input data in an objective and accurate way. This research is led at INRIA (AIR project) in collaboration with GMD Institute for Computer Architecture and Software Technology (FIRST), Berlin within the ERCIM working group Environmental Modelling.

National audience; This paper is concerned with three main problems of image processing occuring on temporal sequences of satellite oceanographic images: approximate localization of the interesting structures on the images; segmentation or determination of the boundary of the structures; and temporal tracking of these boundaries to illustrate their evolution. In this paper, application is done on vortices and results are displayed all over the paper. For this purpose, we propose a new method for optical flow computation and interpretation and a geometric modelling of the structures. Oceanographic images obtained from environmental satellite platforms present a new challenge in computer science. The huge amount of data collected each day and the need for characterizing some specific structures on these images for oceanographic monitoring justify our approach for the detection and tracking of vortices on oceanographic images.

International audience; The NOAA-AVHRR sensor provides daily acquisitions which may be used for vegetation growth monitoring. However, the coarse resolution of these data causes a problem of pixel heterogeneity. The pixel radiometric value is, in fact, a composition of individual responses from the different land covers found within the pixel's surface. In this paper we assume a linear relation. The simultaneous use of NOAA-AVHRR temporal series and of high spatial resolu-tion ground data makes it possible to estimate the pure NOAA reflectances in the visible and near infrared wavelengths for each vegetation type. These val-ues are then combined to compute the NDVI temporal profile which describes the seasonal cycle of the studied crops. The proposed method is applied on the region of Chartres in order to describe the major crops temporal behavior.

International audience; The study of vegetation repartition and evolution is a wide research field and application task for environmental modeling. The objective of our study is to discriminate different vegetation types by their temporal evolution. For that purpose, we use two different sensors: the SPOT sensor provides monthly data with a sufficient spatial resolution, while the NOAA sensor provides daily data, but with a poor spatial resolution. Combining these two complementary sensors seems to be a promising way to lead the study. We propose here a three-step experiment showing that the simultaneous use of these two sensors allows us to obtain a fine segmentation of land cover.

International audience; This paper deals with the problem of automatic interpretation of oceanographic images for vortices detection, modelization and tracking. We present a framework allowing the vortices detection. The processing is split into three parts: apparent motion computation from the image sequence, local interpretation of apparent motion and geometric modelization of the vortices. This scheme allows an efficient approach for vortices segmentation on very large image sequences. A set of experimental data shows the use of such framework for processing Advanced Very High Resolution Radiometer (AVHRR) and Coastal Zone Color Scanner (CZCS) image sequences

International audience; This paper is concerned with three main problems of image processing occuring on temporal sequences of satellite oceanographic images: approximate localization of the interesting structures on the images; segmentation or determination of the boundary of the structures; and temporal tracking of these boundaries to illustrate their evolution. In this paper, application is done on vortices and results are displayed all over the paper. For this purpose, we propose a new method for optical flow computation and interpretation and a geometric modelling of the structures. Oceanographic images obtained from environmental satellite platforms present a new challenge in computer science. The huge amount of data collected each day and the need for characterizing some specific structures on these images for oceanographic monitoring justify our approach for the detection and tracking of vortices on oceanographic images.

International audience; Sea temperature images coming from infrared sensors (AVHRR) allow the visualization of oceanographic activity at meso-scale (100 km), such as temperature fronts, vortices, ... These phenomena are, by their very nature, deformable, and deserve specific studies. We focused our study on the segmentation of vortices. We present three different methods to do this, using hybrid hyperquadric functions, Markovian framework, and geometrical modelization.