Estimation de la fonction de transfert de modulation à l'aide d'un réseau de neurones

- La qualité des images des caméras satellitaires est régulièrement contrôlée en vol. La fonction de transfert de modulation (FTM) est un des critères de qualité image. Sa connaissance permet d'estimer ou de comparer les performances en vol de différents satellites et est utile pour calculer des filtres de déconvolution. Nous proposons une méthode permettant d'évaluer en vol la fonction de transfert de modulation d'un instrument de manière univariante, c 'est-à-dire à partir d'une image quelconque, sans image de référence. Pour effectuer cette évaluation, nous étalonnons préalablement un modèle mathématique sur des images dont le contenu en terme de paysage et de FTM est connu. En raison de la complexité du phénomène à modéliser, nous avons choisi d'utiliser un réseau de neurones artificiels (RNA). Ce modèle, non linéaire, a l'avantage d'être un excellent interpolateur et de permettre l'utilisation de méthodes très simples d'étalonnage de ses paramètres. En pratique, il s'agit dans un premier temps de caractériser le problème en séparant au mieux, d'une part, l'information liée à la FTM et, d'autre part, l'information liée au paysage présent dans une image quelconque afin d'extraire une signature mathématique paysage/FTM. Ensuite, le RNA apprend à dissocier finement, grâce à des exemples connus, la FTM et la structure du paysage à l'aide de la signature paysage/FTM. Finalement, le RNA est utilisé de façon autonome sur des images inconnues pour estimer la FTM. Un point primordial de cette approche univariante est de dissocier au mieux la structure du paysage et la FTM. La validation de la méthode est effectuée au moyen de simulations d'images

A NEW SATELLITE IMAGERY STEREO PIPELINE DESIGNED FOR SCALABILITY, ROBUSTNESS AND PERFORMANCE

Abstract. This paper presents a new Multiview Stereo Pipeline (MVS), called CARS, dedicated to satellite imagery. This pipeline is intended for massive Digital Surface Model (DSM) production and has therefore been designed to maximize scalability robustness and performance. Those two properties have driven the design of the workflow as well as the choice of algorithms and parameter trends, making our pipeline unique with respect to existing solutions in literature. This paper intends to serve as a reference paper for the pipeline implementation, and therefore provides a detailed description of algorithms and workflow. It also demonstrates the pipeline robustness and stability in several use cases, and compares its accuracy with the state-of-the-art pipelines on a reference dataset. Document type: Articl

DEM RECONSTRUCTION USING LIGHT FIELD AND BIDIRECTIONAL REFLECTANCE FUNCTION FROM MULTI-VIEW HIGH RESOLUTION SPATIAL IMAGES

This paper presents a method for dense DSM reconstruction from high resolution, mono sensor, passive imagery, spatial panchromatic image sequence. The interest of our approach is four-fold. Firstly, we extend the core of light field approaches using an explicit BRDF model from the Image Synthesis community which is more realistic than the Lambertian model. The chosen model is the Cook-Torrance BRDF which enables us to model rough surfaces with specular effects using specific material parameters. Secondly, we extend light field approaches for non-pinhole sensors and non-rectilinear motion by using a proper geometric transformation on the image sequence. Thirdly, we produce a 3D volume cost embodying all the tested possible heights and filter it using simple methods such as Volume Cost Filtering or variational optimal methods. We have tested our method on a Pleiades image sequence on various locations with dense urban buildings and report encouraging results with respect to classic multi-label methods such as MIC-MAC, or more recent pipelines such as S2P. Last but not least, our method also produces maps of material parameters on the estimated points, allowing us to simplify building classification or road extraction

L’industrie de la laine en Languedoc depuis la préhistoire jusqu’à nos jours

International audienc

Improving the accuracy of a Shack-Hartmann wavefront sensor on extended scenes

International audienceIn order to achieve higher resolutions, current earth-observation satellites use larger lightweight main mirrors which are usually deformed over time, impacting on image quality. In the context of active optics, we studied the problem of correcting this main mirror by performing wavefront estimation in a closed loop environment. To this end, a Shack-Hartman wavefront sensor (SHWFS) used on extended scenes could measure the incoming wavefront. The performance of the SHWFS on extended scenes depends entirely on the accuracy of the shift estimation algorithm employed, which should be fast enough to be executed on-board. In this paper we specifically deal with the problem of fast accurate shift estimation in this context. We propose a new algorithm, based on the global optical flow method, that estimates the shifts in linear time. In our experiments, our method proved to be more accurate and stable, as well as less sensitive to noise than all current state-of-the-art methods. 1. Introduction Adaptive optics was originally developed for the field of astronomy to remove image aberrations induced by wavefronts propagating through Earth's atmosphere. Its task is to correct the aberrations of an incoming wavefront by using a deformable mirror in order to compensate for the distortion. It has been shown that adaptive optics allows to reduce these aberrations thus improving the image quality [1]. A key component of an adaptive optics system is the wavefront sensing mechanism. In astronomical imaging a wavefront sensing device is frequently used in conjunction with a deformable mirror in order to correct the undesired effects of atmospheric turbulence, thus improving the quality of sensed images. A Shack-Hartmann wavefront sensor (SHWFS) is one of such devices. It uses an array of lenslets to measure the deformation of the incoming wavefront. The shift of each lenslet focal plane image is proportional to the mean slope of the wavefront in the subaperture onto this lenslet, thus allowing to obtain a discrete local approximation of the slope of the wavefront, as shown in Fig. 1a. The measured slopes are then used to approximate the actual wavefront. This deformation is usually measured using point sources such as a star. Adaptive optics can also be applied in the context of earth-observation satellites [2, 3]. In this setting, the problem of atmospheric turbulence is negligible. However, lightweight mirrors are required to increase the resolution, since mission costs are driven by the payload weight. The drawback of such mirrors is that time-varying deformations due to thermal effects and vibratio

PLEIADES-HR INNOVATIVE TECHNIQUES FOR GEOMETRIC IMAGE QUALITY COMMISSIONING

Since the beginning of 2012, the first Pleiades-HR satellite of the program conducted by the French National Space Agency, CNES, delivers 20 km wide color scenes with a 70 cm ground sampling distance. A second satellite should be launched in 2013 which will achieve an almost world-wide coverage with a revisit interval of 24h. The assessment of the image quality and the calibration operation have been performed by CNES Image Quality team during the 6 month commissioning phase that followed the satellite launch. The geometric commissioning activities consist in improve the geometric quality of the images in order to meet very demanding specifications as localization accuracy, local coherence, dynamic stability, length alteration … This goal has been achieved through the implementation of new methods of calibration and performance assessment. Some of these methods are based on the exploitation of very specific satellite acquisitions that have been achieved thanks to the amazing agility of the Pleiades satellite. Thus, many stars acquisitions and very slow earth pictures have been processed to characterize dynamic phenomena. Similarly, “along-cross track” pairs have been exploited to improve the accuracy of the focal plane description. This paper deals with these new methods. It describes their accuracy and their operational interests

Improving a shoreline forecasting model with Symbolic Regression

International audienceGiven the current context of climate change and the increasing population densities at coastal zones around the globe, there is an increasing need to be able to predict the development of our coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. Symbolic Regression (SR) is a type of Machine Learning algorithm that aims to find interpretable symbolic expressions that can explain relations in the data. In this work, we aim to study the problem of forecasting shoreline change using SR. We make use of Cartesian Genetic Programming (CGP) in order to encode and improve upon ShoreFor, a physical shoreline prediction model. During training, CGP individuals are evaluated and selected according to their predictive score at five different coastal sites. This work presents a comparison between a CGP-evolved model and the base ShoreFor model. In addition to evolution's ability to produce well-performing models, it demonstrates the usefulness of SR as a research tool to gain insight into the behaviors of shorelines in various geographical zones

Improving a shoreline forecasting model with Symbolic Regression

Given the current context of climate change and the increasing population densities at coastal zones around the globe, there is an increasing need to be able to predict the development of our coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. Symbolic Regression (SR) is a type of Machine Learning algorithm that aims to find interpretable symbolic expressions that can explain relations in the data. In this work, we aim to study the problem of forecasting shoreline change using SR. We make use of Cartesian Genetic Programming (CGP) in order to encode and improve upon ShoreFor, a physical shoreline prediction model. During training, CGP individuals are evaluated and selected according to their predictive score at five different coastal sites. This work presents a comparison between a CGP-evolved model and the base ShoreFor model. In addition to evolution's ability to produce well-performing models, it demonstrates the usefulness of SR as a research tool to gain insight into the behaviors of shorelines in various geographical zones

Optimisation de la méthode de débruitage Non Local Bayes pour les images satellites optiques à très haute résolution spatiale : applications dans le contexte des missions Pléiades et Spot World Heritage

International audienc

2D Sub-Pixel Disparity Measurement Using QPEC / Medicis

In the frame of its earth observation missions, CNES created a library called QPEC, and one of its launcher called Medicis. QPEC / Medicis is a sub-pixel two-dimensional stereo matching algorithm that works on an image pair. This tool is a block matching algorithm, which means that it is based on a local method. Moreover it does not regularize the results found. It proposes several matching costs, such as the Zero mean Normalised Cross-Correlation or statistical measures (the Mutual Information being one of them), and different match validation flags. QPEC / Medicis is able to compute a two-dimensional dense disparity map with a subpixel precision. Hence, it is more versatile than disparity estimation methods found in computer vision literature, which often assume an epipolar geometry. CNES uses Medicis, among other applications, during the in-orbit image quality commissioning of earth observation satellites. For instance the Pléiades-HR 1A & 1B and the Sentinel-2 geometric calibrations are based on this block matching algorithm. Over the years, it has become a common tool in ground segments for in-flight monitoring purposes. For these two kinds of applications, the two-dimensional search and the local sub-pixel measure without regularization can be essential. This tool is also used to generate automatic digital elevation models, for which it was not initially dedicated. This paper deals with the QPEC / Medicis algorithm. It also presents some of its CNES applications (in-orbit commissioning, in flight monitoring or digital elevation model generation). Medicis software is distributed outside the CNES as well. This paper finally describes some of these external applications using Medicis, such as ground displacement measurement, or intra-oral scanner in the dental domain