Natural Color Satellite Image Mosaicking Using Quadratic Programming in Decorrelated Color Space

International audienceGenerating mosaics of orthorectified remote sensing images is a challenging task because of the colorimetric differences between adjacent images introduced by land use, surface illumination, atmospheric conditions, and sensor. Most of the existing color correction methods involve pairwise techniques, which are limited when the collection of images is large with numerous overlaps. Besides , available techniques do not operate in a color space suited for true-color processing. This paper presents a simple and robust method to perform the global colorimetric harmonization of multiple overlaping remote sensing images in natural colors (RGB). Our parameter-free method deals simultaneously with any number of images, with any spatial layout, and without any single reference image. It is based on the resolution of a quadratic programming optimization problem. It operates in the lαβ decorrelated color space, which is well suited for human vision of natural scenes. The results obtained from the mosaicking of 132 RapidEye color orthoimages over mainland France demonstrate good potential for performing colorimetric harmonization automatically and effectively.La génération de mosaïques d'images orthorectifiées en télédétection est une tâche difficile en raison des différences colorimétriques entre les images adjacentes introduites par l'utilisation des terres, l'éclairage de surface, les conditions atmosphériques, et le capteur. La plupart des méthodes existantes de correction des couleurs impliquent des techniques par paires, qui sont limitées lorsque la collection d'images est grand avec de nombreux chevauchements. En outre, les techniques disponibles ne fonctionnent pas dans un espace de couleur adapté pour le traitement des vraies couleurs. Cet article présente une méthode de androbust simple à réaliser l'harmonisation colorimétrique mondial de distance chevauchent plusieurs images de détection dans des couleurs naturelles (RGB). Nos méthode sans paramètres traite simultanément avec un certain nombre d'images et avec un aménagement de l'espace, et sans aucune image de référence unique. Elle est basée sur la résolution d'un problème d'optimisation quadratique programmation (QP). Elle opère dans l'espace de couleur de lab décorrélés, ce qui est bien adapté pour la vision humaine de scènes naturelles. Les résultats obtenus à partir du mosaïquage de 132 RapidEye ortho-images de couleurs plus France métropolitaine démontrent un bon potentiel pour réaliser l'harmonisation colorimétrique automatiquement et efficacement

Validation numérique de l'approche d'analyse de sensibilité spatiale de Lilburne et Tarantola

On s'intéresse ici aux diverses méthodes proposées pour évaluer la sensibilité d'une sortie de modèle Y = f(X1; ... ;Xk) à l'incertitude qui pèse sur un facteur d'entrée Xi distribué spatialement. On se restreint aux approches sans construction d'une surface de réponse (méta-modélisation). L'objet de ce document est : 1) de valider de manière empirique la méthode proposée par Lilburne & Tarantola (2010) 2) de comparer (de manière numérique) cette méthode avec les autres méthodes On étudie pour cela trois fonctions tests

Comparison of Three Spatial Sensitivity Analysis Techniques

International audienceThis paper compares the spatial Sobol' sensitivity approach to two other sensitivity analysis techniques on a model with spatially distributed inputs. The comparison is performed on AquiferSim, a model that simulates groundwater flow and nitrate transport from paddock to aquifer

Analyse de sensibilité globale d'un modèle spatialisé pour l'évaluation économique du risque d'inondation

L'analyse de sensibilité globale peine à se développer dans le champ de la modélisation environnementale. Dans sa formulation initiale, elle est limitée à l'étude de modèles Y = f (X1; : : : ;Xp) où les variables d'entrée Xj et la sortie Y sont scalaires, alors que nombre de modèles environnementaux incluent une dimension spatiale marquée, soit qu'ils fassent appel à des cartes comme variables d'entrée, soit que leurs sorties soient distribuées spatialement. Au travers d'une étude de cas détaillée, nous présentons dans cet article une extension de l'analyse de sensibilité globale à l'étude de modèles spatialisés. Le modèle étudié, nommé ACB-DE, est un outil d'évaluation économique du risque d'inondation. Il est ici appliqué sur la basse-vallée de l'Orb (Hérault). Des spécifications spatialisées de l'incertitude sont utilisées pour générer un nombre fini de réalisations aléatoires équiprobables des variables d'entrée qui sont des cartes : les effets de structure spatiale ou d'auto-corrélation dans ces cartes peuvent ainsi être pris en compte. La réalisation de cartes d'indices de sensibilité permet ensuite d'étudier les sorties spatialisées du modèle ACB-DE et de rendre compte de la variabilité spatiale des indices de Sobol. L'influence relative des variables d'entrée à différentes échelles d'étude est analysée par la réalisation de cartes d'indices de sensibilité de résolution croissante. L'analyse réalisée permet d'identifier les variables d'entrée incertaines qui expliquent la plus grande part de la variabilité de l'indicateur économique fourni par le modèle ACB-DE ; elle apporte un éclairage nouveau sur le choix de l'échelle adéquate de représentation spatialisée de cet indicateur selon la précision des variables d'entrée. L'approche proposée pourrait être aisément appliquée à d'autres modèles spatialisés peu coûteux en temps de calcul. / Variance-based Sobol' global sensitivity analysis (GSA) was initially designed for the study of models with scalar inputs and outputs, while many models in the environmental field are spatially explicit. As a result, GSA is not a common practise in environmental modelling. In this paper we describe a detailed case study where GSA is performed on a spatially dependent model for flood risk economic assessment on the Orb valley (southeast France). Spatial input factors are handled by associating randomly generated map realizations to scalar values sampled from discrete uniform distributions. The realisations of random input maps can be generated by any method including geostatistical simulation techniques, allowing for spatial structure and auto-correlation to be taken into account. The estimation of sensitivity indices on ACB-DE spatial outputs makes it possible to produce maps of sensitivity indices. These maps describe the spatial variability of Sobol' indices. Sensitivity maps of different resolutions are then compared to discuss the relative influence of uncertain input factors at different scales

Latin Hypercube Sampling of Gaussian random field for Sobol' global sensitivity analysis of models with spatial inputs and scalar output

4 pagesInternational audienceThe variance-based Sobol' approach is one of the few global sensitivity analysis methods that is suitable for complex models with spatially distributed inputs. Yet it needs a large number of model runs to compute sensitivity indices: in the case of models where some inputs are 2D Gaussian random fields, it is of great importance to generate a relatively small set of map realizations capturing most of the variability of the spatial inputs. The purpose of this paper is to discuss the use of Latin Hypercube Sampling (LHS) of geostatistical simulations to reach better efficiency in the computation of Sobol' sensitivity indices on spatial models. Sensitivity indices are estimated on a simple analytical model with a spatial input, for increasing sample size, using either Simple Random Sampling (SRS) or LHS to generate input map realizations. Results show that using LHS rather than SRS yields sensitivity indices estimates which are slightly more precise (smaller variance), with no significant improvement of bias

Analyse de sensibilité globale d'un modèle d'évaluation économique du risque d'inondation

International audienceVariance-based Sobol' global sensitivity analysis (GSA) was initially designed for the study of models with scalar inputs and outputs, while many models in the environmental eld are spatially explicit. As a result, GSA is not a common practise in environmental modelling. In this paper we describe a detailed case study where GSA is performed on a spatially dependent model for flood risk economic assessment on the Orb valley (southeast France). The realisations of random input maps can be generated by any method including geostatistical simulation techniques, allowing for spatial structure and auto-correlation to be taken into account. The estimation of sensitivity indices on ACB-DE spatial outputs makes it possible to produce maps of sensitivity indices. These maps describe the spatial variability of Sobol' indices. Sensitivity maps of di fferent resolutions are then compared to discuss the relative influence of uncertain input factors at diff erent scales

Sensitivity analysis of spatial models using geostatistical simulation

International audienceGeostatistical simulations are used to perform a global sensitivity analysis on a model Y = f(X1 ... Xk) where one of the model inputs Xi is a continuous 2D-field. Geostatistics allow specifying uncertainty on Xi with a spatial covariance model and generating random realizations of Xi. These random realizations are used to propagate uncertainty through model f and estimate global sensitivity indices. Focusing on variance-based global sensitivity analysis (GSA), we assess in this paper how sensitivity indices vary with covariance parameters (range, sill, nugget). Results give a better understanding on how and when to use geostatistical simulations for sensitivity analysis of spatially distributed models

Ranking sources of uncertainty in flood damage modelling: a case study on the cost-benefit analysis of a flood mitigation project in the Orb Delta, France

International audienceCost-benefit analyses (CBA) of flood management plans usually require estimating expected annual flood damages on a study area, and rely on a complex modelling chain including hydrological, hydraulic and economic modelling as well as GIS-based spatial analysis. As most model-based assessments, these CBA are fraught with uncertainty. In this paper, we consider as a case-study the CBA of a set of flood control structural measures on the Orb Delta, France. We demonstrate the use of variance-based global sensitivity analysis (VB-GSA) to i) propagate uncertainty sources through the modelling chain and assess their overall impact on the outcomes of the CBA, and ii) rank uncertainty sources according to their contribution to the variance of the CBA outcomes. All uncertainty sources prove to explain a significant share of the overall output variance. Results show that the ranking of uncertainty sources depends not only on the economic sector considered (private housing, agricultural land, other economic activities), but also on a number of averaging-out effects controlled by the number and surface area of the assets considered, the number of land use types or the number of damage functions

How far spatial accuracy governs land-use changes monitoring frequency: the urban sprawl monitoring example

International audienceIn this paper, we illustrate how far spatial accuracy of a land-use map governs land-use changes monitoring frequency on a urban sprawl monitoring case study. From a specific Monte Carlo approach propagating uncertainties, conf4idence curves for minimal monitoring frequency to detect significant changes in urban sprawl indicators were built. Results showed that frequency decreased when uspscaling indicators but it also showed very low monitoring frequency for indicators at the lower level. INTRODUCTION When setting up land-use monitoring systems, spatial uncertainties and their impact on the detection of indicator changes are usually ignored. To capture a significant change in land-use indicators is thus strongly related to its spatial resolution, the velocities of the process it represents and the accuracy of the used indicators. As a consequence, the required monitoring land-use change frequency, corresponding to the mimimum time step to ensure a significant change in indicators, also depends on these three factors: indicator spatial resolution, change process velocity and spatial indicator accuracy

Analyzing urban sprawl indicators under uncertainties

International audienceUrban sprawl causes the sealing of lands closest to the urban centers by transforming productive agricultural fields into impervious areas, with numerous economic, social and environmental impacts. Integrated monitoring is important to help urban policies and urban sprawl modelling, proposing urban sprawl indicators with combining spatial and social aspects of the phenomenon by spatial operators in GIS. For that, geographical information of sealing patches and territorial social data are used, but spatial uncertainties and their impact on the detection of indicator changes are usually ignored. The paper proposes (1) to evaluate uncertainties of indicator spatial and (2) to analyze the effect of upscaling on these uncertainties. The method used proposes to create impervious polygons according to their measured geometric and thematic uncertainties using a Monte Carlo simulation approach and to simulate social data according to census uncertainties. Impervious polygons are used in a closing operation, with different radius values, required to map morphological urban areas. The case study focused on three indicators (area, dispersion coefficient and population density) of the morphological urban areas for four administrative levels of administrative territorial units of Languedoc-Roussillon region, France. Results show that indicator uncertainties are generally higher for less densely populated areas than for the others at the finest territorial level, that the closing radius had a slightly influence on indicator uncertainties, and that uncertainties decreases with the upper territorial entities