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Resume Objectif L’objectif de cette etude est d’evaluer l’exactitude de la correction d’attenuation (CA) en tomographie a emission de positons (TEP) utilisant des donnees tomodensitometriques (TDM). Apres reduction de taille de matrice et lissage des images TDM, une courbe de conversion trilineaire est appliquee ; elle permet de convertir les unites Hounsfield (UH) en coefficients d’attenuation a 511 keV (CAL511 keV). C’est en particulier sur la pertinence de cette courbe de conversion que ce travail a porte. Materiels et methodes Les donnees TDM d’un fantome de densite compose de 17 inserts equivalents-tissus ont ete acquises sur un TEP-TDM Discovery ST4®. Les donnees ont ete analysees avec une version modifiee d’un logiciel d’automatisation du controle de qualite des tomodensitometres qui permet d’obtenir directement les CAL511 keV en fonction des UH. Afin d’evaluer la performance de la correction d’attenuation en clinique, une serie d’images de patients ayant beneficie d’un examen TEP-TDM a egalement ete etudiee. Resultats Les valeurs des CAL511 keV mesurees avec Artiscan® sont en bonne correlation avec les valeurs attendues, excepte pour l’insert d’os dense de 10 mm de diametre pour lequel la valeur est significativement inferieure a celle attendue et cet ecart devenant non significatif avec un insert specifique de 30 mm de diametre. En faisant varier la force du lissage gaussien, nous avons montre que cet effet pouvait etre minimise en reduisant la largeur a mi-hauteur (LMH) du filtre applique. Les mesures effectuees a partir des donnees de patients ont confirme une sous-estimation des valeurs de CAL511 keV des hautes densites tissulaires. Conclusion Nous avons developpe une procedure de controle de qualite de la courbe de conversion. Une sous-estimation des valeurs de CAL en relation avec la taille d’echantillon a ete mise en evidence et a pu etre rendue non significative en reduisant le lissage des coupes TDM avant conversion.

To carry out functioning and dynamic vegetation studies, a temporal analysis is needed. So far, only data provided by the National Oceanic and Atmospheric Administration (NOAA) satellites with Advanced Very High Resolution Radiometer (AVHRR) sensors offer the required temporal resolution, but their spatial resolution is coarse (1.1 km). But, in many situations, the vegetation cover is heterogeneous and the 1.1 km AVHRR pixel contains several types of land use radiometrically different and is, in fact, a mixed pixel. Thus, the reflectance and consequently deduced parameters (NDVI, LAI, etc.) measured by AVHRR is actually average value and does not represent a value for each vegetation class present in the pixel. The objective is to extract the reflectance of each vegetation class from the mixed pixel using NOAA-AVHRR data and SPOT-HRV data simultaneously which give the proportions of each type of vegetation inside the mixed pixel through a classification map. The paper presents a method for radiometrically...

When estimating agricultural production, we are faced with a problem of scale, be they scales of observation, modeling, or representation. In the context of the farming landscape of Europe, the relationships between scales and modeling take on special importance as the aim is to extract information on a smaller scale than the scale of observation, the latter necessarily being large relative to the average size of fields in order to obtain the required time resolution. This article presents a method for unmixing coarse resolution signals (of the NOAA-AVHRR type) through the use of multiple linear regression. This allows the signal for each mixed coarse resolution pixel to be broken down thanks to a knowledge of land use and a linear mixture model. It is then possible to calculate the individual radiometric contribution of each constituent of a mixed pixel. The work presented here is an assessment of the performance of this method using SPOT-HRV data, degraded to simulate the coarse resolution and as normally provided to produce the land use information. Simulation with SPOT-HRV data also enables various parameters to be calculated, from which it is possible to verify the interest and reliability of multiple linear regression as a method for spectral unmixing.

Nowadays, the emergence of very high resolution satellite (VHRS) images imposes to reconsider the methods used to describe surfaces and objects in remote sensing images. The precision provided by new sensors allow the capability to discriminate small entities and non-observable objects until now particularly in urban areas. In this context, we show that mathematical morphology can enhance methodologies for the processing and analysis of urban remote sensing data. In this paper, we first present a self-adapting method of segmentation of VHRS images. The methodology exploits adjacency graphs and its corresponding morphological processing to describe the scene. Results of this segmentation methodology allows us to obtain suitable and significant connected components. In a second step, the interpretation of the segmented images is realized by means of features extraction according to intrinsic and extrinsic information associated to each region. Geometric and morphological features are first extracted using very simple morphological operations. The image is then described in terms of spatial relations between entities. The proposed methodology is applied to VHRS images interpretation in order to characterize urban areas. We illustrate the usefullness of mathematical morphology tools with PLEIADES HR satellite images.

In this paper, a belief-based pixel labelling strategy is introduced and applied to image segmentation. The procedure exploits the results of a K-means clustering algorithm for first quantifying the membership degree of each pixel to a region in the image. Belief functions are then used to quantify the uncertainty about the pixel labelling. This theoretical framework allows to manage imprecise and uncertain information extracted from spatial neighbors. Using this strategy (classification + post-labelling), the segmentation scheme allows to perform a complementary approach combining region segmentation and edge detection. Segmentation results on different kinds of image are presented and allow to highlight the algorithm performances. Finally, the application of this methodology is also presented in case of 3D medical imaging and multi-spectral satellite images.

A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The two reflective channels 0.58-0.68 /spl mu/m and 0.725-1.1 /spl mu/m were used to run a least mean squares to generate the radiometric information needed to calculate the contribution of each cover type present in the coarse resolution signal and so to plot their temporal profile of NDVI for a complex area in Burkina-Faso. >

The description of ecosystem structure and functioning is necessary in view of the monitoring of vegetation resources dynamics. It is therefore necessary to have spatial and temporal resolutions allowing respectively the characterization of the horizontal structure and the functional cycles. The images from NOAA-AVHRR are largely used for monitoring vegetation. Mixed pixel reflectance obtained using moderate spatial resolution instrument can be approximated by using a linear mixture model. The land use information obtained from high spatial resolution data allows the deconvolution of the response of a mixed pixel into the different components responses. A methodological adjustment has been performed over a spatially complex area in the West Africa. For this study, M.S.R data have been obtained through the degradation of SPOT images. This simulation gave the opportunity to check all the parameters of the model and therefore to testify its reliability and its sensitivity. >

This study concerns the integration of remote sensing data at high temporal resolution associated with agro-meteorological data in the modelisation of agricultural production, at regional scale. Therefore, three objectives have been defined. The passing from a regional analysis of vegetation monitoring to a crop analysis during the vegetative cycle; the quantification of crop production thanks to on adding of radiometric elements (calculated at the previous step) in a productivity model, coming from Monteith model; and the spatialisation of production data thanks to the use of satellite imagery.

International audience; The Regional Atmospheric Modeling System (RAMS) is used to assess a realistic estimate of emissions by savanna fires from NOAA advanced very high resolution radiometer (AVHRR) satellite imagery during the Experiment for Regional Sources and Sinks of Oxidants (EXPRESSO) campaign. The daily amounts of gases released by biomass burning depend on burnt biomaterial, the mass of which is estimated from two independent methods: classification of vegetation formations (CV) or net primary productivity (NPP). The area of the burnt zone is derived from satellite imagery. The percent of burnt biomass or combustion factor (a) is determined by an adjustment procedure using two model runs and assumed to be 40%, a value consistent with the biomass wetness. The simulated CO redistribution is compared with aircraft measurements (tracks and profiles). Two events of 4 and 5 days are examined, where flights above forest and savanna zones have been performed. In this study we show that the main mechanisms of the chemical species spatial redistribution originates from mesoscale features. The RAMS results are in rather good agreement with the aircraft measurements (vertical profiles or horizontal legs) over forest and savanna. The difference between the CO simulated fields obtained from CV or NPP is weak (about 20% or 30%), and it is difficult to discriminate the best method because this difference is generally inferior to the gap between model and observations. One may consider this study as a preliminary approach to improve the estimate of released gases by biomass burning with the help of mesoscale modeling and the knowledge of the real sources.

The radar response to soil surface was investigated in 1979 using a scatterometer operating at 1.5, 3, 4.5 and 9 GHz. Analysis of data in terms of the effect of frequency, polarization, incidence, soil moisture and surface roughness on the radar response is presented. Characterization of soil parameters suitable for data interpretation is developed.

Textural functions and computer cartography of land use. — This article contains the major results obtained through automatic cartography and textural analysis in remote sensing in the context of an experiment carried out by the scientific committee «Télédétection spatiale» in the CNRS in 1981. Four essential features should be inderlined. The first concerns the validation of a model of automatic cartography in its application to a land use inventory of a drainage basin (the Aveyron). The second lies in a proposal for an original approach in the analysis of texture and structure of images. The third is the definition of models of characterisation of heterogeneity through measures of mixing and fragmentation. The fourth lies in the definition of form extraction models through operations in mathematical morphology., Cet article présente les principaux résultats obtenus sur la cartographie automatique et l'analyse de texture en télédétection dans le cadre d'une expérience retenue par le comité scientifique Télédétection Spatiale du CNRS en 1981. Quatre points essentiels ont été dégagés. Le premier concerne la validation d'un modèle de cartographie automatique dans son application à l'inventaire de l'occupation des terres d'un bassin versant (l'Aveyron). Le second consiste en une proposition d'approche originale pour l'analyse de la texture et de la structure des images. Le troisième est la définition de modèles de caractérisation de l'hétérogénéité par des mesures de mélange et de fragmentation. Le quatrième est la définition de modèles d'extraction de formes par des opérations de morphologie mathématique., Flouzat G.,Guichou C.,Merghoub Y.,Guillemyn D.Flouzat G., Guichou C., Merghoub Y., Guillemyn D. Recherche de fonctions texturantes et cartographie automatique de l'occupation des terres. In: L'Espace géographique, tome 13, n°3, 1984. pp. 241-249.

International audience

Our goal is demonstrate the applicability of orthogonal wavelet analysis to bring remote sensed images of different resolutions to a common level of spatial accuracy. The decomposition of an image in a orthogonal wavelet representation and it’s reconstruction are illustrate, and the error of reconstruction evaluate and justified. Two applications intend to show the viability of this project : 1) a new method to sinthetize images at 10 meter resolution from SPOT multispectral (20 m) and panchromatic (10 m) images; 2) three images initially scanned in three different steps are decomposed to form a single three band image at the lowest resolution, and compared to the one originally scanned at this resolution level.

This paper presents a geometric approach to the processing of two-dimensional geophysical data. The study has been carried out as an alternative to harmonic analysis, and it uses the properties of an image analysis method: mathematical morphology. Some examples illustrate the most interesting characteristics of the morphological approach which is then applied to the analysis of the gravity anomalies in the Indian Ocean. Two kinds of results have been obtained. From a methodological point of view, morphological analysis shows the interest of a method that does not need either a priori information on the spatial characteristics of the objects under study or global processings needing long computer times. From the geophysical point of view, the extraction and characterization of the undulations allow one to retrieve the geophysical phenomena recently discovered in this region and to reveal some features and structures that the harmonic analysis methods have not yet detected.