Classification automatique de sources astronomiques par cartes auto-organisatrices

- Cet article présente le développement d'un classifieur automatique basé sur les cartes auto-organisatrices de Kohonen pour les objets présents sur les images astronomiques. L'originalité de la méthode consiste à utiliser un classifieur non supervisé et à lui présenter directement les pixels constituant les objets à étudier, sans utiliser de paramètres décrivant ces objets. Nous présentons différentes normalisations possibles pour ces descripteurs originaux. Le classifieur ainsi construit est testé sur des images astronomiques simulées et réelles (images d'une base de données astronomiques et du télescope automatique TAROT). Pour les deux types d'images, la méthode est aussi performante que les méthodes supervisées. Pour les images TAROT, qui sont des images très bruitées, la définition d'un seuil de classification au-delà duquel l'efficacité du classifieur n'est plus acceptable est nécessaire

Une nouvelle méthode de reconnaissance des champs pour les images astronomiques

Cet article présente un nouvel algorithme de reconnaissance des champs pour les images astronomiques. La méthode est basée sur une analyse multirésolution des deux images à mettre en correspondance : images test et référence. Les structures de chaque image aux différentes échelles sont obtenues par transformée en ondelettes. Après sélection des coefficients les plus significatifs, on utilise un algorithme génétique pour faire converger les deux structures. Nous appliquons cette méthode sur les images du télescope automatique TAROT (Télescope à Action Rapide pour les Objets Transitoires). La méthode semble plus robuste et plus rapide que les méthodes existantes

Optimisation jointe de la chaîne codage/débruitage pour les images satellite

National audienceIn this paper, we propose to study the problem of optimal noisy source coding/denoising. This problem can be formulated as an optimization problem where the criterion to minimize is the global distortion, that is the error between the noise-free image and the denoised image. This problem is challenging since a global optimization is usually difficult to perform as the global fidelity criterion needs to be optimized in the same time over the sets of both coding and denoising parameters. We show here how to express the global distortion in closed-form and we present an algorithm to minimize this distortion with respect to these parameters. We show results of this joint optimization algorithm on classical test images and on a high dynamic range image, visually and in a rate-distortion sense.Dans ce travail, nous proposons d'étudier le problème du codage/débruitage optimal d'une source bruitée. Ce problème peut se formaliser comme un problème d'optimisation où le critère à minimiser est la distorsion globale, c'est-à-dire l'erreur entre l'image d'origine non bruitée et l'image décodée débruitée. Ce problème est complexe à traiter car une optimisation globale est habituellement difficile à effectuer puisque le critère global doit être optimisé en même temps par rapport aux paramètres de codage et de débruitage. Nous montrons ici comment écrire analytiquement les différents termes de la distorsion globale et nous présentons un algorithme pour minimiser cette distorsion par rapport à ces paramètres. Nous montrons des résultats de cet algorithme d'optimisation jointe sur des images classiques et sur une image satellite haute dynamique, visuellement et d'un point de vue débit-distorsion

Post-transformée dans le domaine ondelettes appliquée à la compression d'images satellite

Cet article s'intéresse à la compression d'images satellite. La technique proposée met en œuvre la transformée en ondelettes couplée à une post-transformée par blocs sélectionnée dans un dictionnaire de bases. L'originalité du travail réside dans le dictionnaire utilisé. Ce dictionnaire est constitué de bases construites à partir de l'Analyse en Composantes Principales (ACP) sur une base d'apprentissage de blocs de coefficients d'ondelettes. La post-transformée proposée permet d'éliminer les corrélations entre coefficients d'ondelettes voisins identifiés dans [1]. Elle bénéficie aussi de la propriété de concentration d'énergie de l'ACP qui est exploitée au moment du codage

Joint coding-denoising optimization of noisy images

In this paper, we propose to study the problem of noisy source coding/denoising. The challenge of this problem is that a global optimization is usually difficult to perform as the global fidelity criterion needs to be optimized in the same time over the sets of both coding and denoising parameters. Most of the bibliography in this domain is based on the fact that, for a specific criterion, the global optimization problem can be simply separated into two independent optimization problems: The noisy image should be first optimally denoised and this denoised image should then be optimally coded. In many applications however, the layout of the acquisition imaging chain is fixed and cannot be changed, that is a denoising step cannot be inserted before coding. For this reason, we are concerned here with the problem of global joint optimization in the case the denoising step is performed, as usual, after coding/decoding. In this configuration, we show how to express the global distortion as a function of the coding and denoising parameters. We present then an algorithm to minimize this distortion and to get the optimal values of these parameters. We show results of this joint optimization algorithm on classical test images and on a high dynamic range image, visually and in a rate-distortion sense

Global rate-distortion optimization of satellite imaging chains

International audienceThe joint compression/restoration optimization of a satellite imaging chain is a challenging problem which has been little investigated so far. Some works have been done in designing an optimal coding/decoding structure which takes into account the characteristics of the imaging chain (Parisot, 2001), but, to the best of our knowledge, the study of the global system optimization has not devoted much work; so that each process is usually optimized separately. In this paper, we focus on the global optimization of the satellite imaging chain including both compression and restoration. We propose a closed-form expression of the global distortion as a function of the chain parameters and we show how to optimize this distortion, for a given rate, to obtain the parameters of the restoration and the compression algorithms which lead to the minimal distortion

On the optimization of the satellite imaging chain

In this paper, we focus on the global optimization of the satellite imaging chain. The theoretical analysis of the satellite imaging chain optimization is a difficult problem that needs lot of approximations. In order to consider the complex real satellite imaging chain, we propose to address this problem numerically and we present, based on numerical experiments, techniques to optimize the quality of the reconstructed final image. We first focus on the common question of the position of the restoration step in the imaging chain, that is on-board before coding or on-ground after coding. Then, we present several methods to remove the coding artifacts inherent in wavelet based coder schemes. From these numerical results we propose a new satellite imaging chain and we show visual and rate-distortion results on a real satellite image

Joint coding-denoising optimization of noisy images

In this paper, we propose to study the problem of noisy source coding/denoising. The challenge of this problem is that a global optimization is usually difficult to perform as the global fidelity criterion needs to be optimized in the same time over the sets of both coding and denoising parameters. Most of the bibliography in this domain is based on the fact that, for a specific criterion, the global optimization problem can be simply separated into two independent optimization problems: The noisy image should be first optimally denoised and this denoised image should then be optimally coded. In many applications however, the layout of the acquisition imaging chain is fixed and cannot be changed, that is a denoising step cannot be inserted before coding. For this reason, we are concerned here with the problem of global joint optimization in the case the denoising step is performed, as usual, after coding/decoding. In this configuration, we show how to express the global distortion as a function of the coding and denoising parameters. We present then an algorithm to minimize this distortion and to get the optimal values of these parameters. We show results of this joint optimization algorithm on classical test images and on a high dynamic range image, visually and in a rate-distortion sense

A satellite imaging chain based on the Compressed Sensing technique

International audienceSatellite imaging has been the focus of intense works in remote sensing for the last years. The ability of satellite optical systems to produce high-resolution images has indeed been of a great interest in applications such as change detection or image classification. The design of a satellite acquisition chain is however quite challenging as it involves expensive processes such as sampling and coding. In this work, we investigate the performances of a low-resource satellite imaging chain based on the Compressed Sensing (CS) acquisition technique. We propose a reconstruction algorithm which takes into account the degradations of the satellite imaging chain and we present results on a real satellite data