Vers une modélisation spatio-temporelle en imagerie satellitale

La télédétection est une technique de plus en plus utilisée dans plusieurs domaines, entre autres, la gestion de l'environnement, le suivi et la prévision des catastrophes naturelles et la surveillance dédiée à des applications spécifiques (agricoles, militaires, etc.). Mais les problèmes d'exploitation des images satellitales disponibles, liés en particulier à leur abondance, à leur nature et à leur diversification, ont complexifié le traitement de ces images et ont permis de développer ainsi plusieurs axes de recherche dans ce domaine dans le but d en extraire le maximum de connaissances et d améliorer leur exploitation. L analyse des scènes naturelles dynamiques, issues d une séquence d images satellitales, introduit un volume important d informations et exige une élaboration substantielle à tous les niveaux : pré-traitement, segmentation, reconnaissance et interprétation donnant naissance à de nombreuses approches pour interpréter la dynamique d'une scène. La difficulté dépend de la nature des entités à reconnaître et de la stratégie d interprétation recherchée relative à chaque entité allant de la reconnaissance d objets simples et complexes jusqu à la reconnaissance de scénarios temporels dans un contexte prévisionnel et décisionnel. Nous proposons une approche dont la particularité est d'exploiter un méta-modèle regroupant un modèle de stratégies d interprétation et un modèle décisionnel dont l objectif est l évaluation et la définition des différents états du système en fonction de la perception de bla scène et des connaissances expertes et contextuelles. Au niveau conceptualisation, nous définissons les objectifs de notre système ainsi que les différentes tâches à accomplir tout en respectant les contraintes de régulation, de rétroaction et de contrôle. Au niveau nopérationnalisation, notre système est basé sur un tableau noir hiérarchique multi-agent, ns appuyant en grande partie sur la modélisation d objets sémantiques. Nous considérons ainsi, ,nnon plus l'interprétation des images à un seul niveau, mais à des granularités différentes allant du niveau macro en terme de la séquence d images jusqu au niveau micro en terme de primitives fines, remontant vers un niveau méso regroupant plusieurs primitives donnant naissances aux objets sémantiques. En effet, notre démarche sera orientée objets sémantique multi-facette tenant compte de la dimension radiométrique, spatiale, contextuelle et temporelle. Chaque Objet se situant dans la scène, peut être vu comme un système évolutif qui change d états. La modélisation spatio-temporelle tend à reproduire l évolution en fonction du temps de la dynamique des objets en question. Cette dernière peut être représenté à l aide d un automate où les états correspondent aux stades de l évolution de l objet à identifier. Ces stades constituent les sommets de l'automate et les événements qui relient ces sommets sont représentés par des arcs munis de contraintes d'horloge. Ainsi, la modélisation spatiotemporelle de la scène interprétée est une représentation sous forme d instances d objets traduisant leurs modèles d évolution.The remote sensing technique is used in several domains as environment management, tracking and forecasting natural disaster, agriculture, military applications, etc. Many problems issued from the exploitation of these images due to their abundance, to their nature and their diversification, have complicated their treatment and permitted to develop several axes of research in this domain in order to extract the maximum of knowledge and to improve their exploitation. The analysis of satellite images representing natural and dynamic scenes introduced a very important volume of information and requires a substantial elaboration at all levels: preprocessing, segmentation, recognition and interpretation. The difficulty depends on the nature of entities to recognize and the strategy of interpretation relative to each entity going from the recognition of simple and complex objects until the temporal script recognition in a provisional and decisional context. We propose an approach whose particularity is to exploit a meta-model regrouping an interpretation strategies model and a decisional model whose objective is the assessment and the definition of the different states of the system according to the scene perception and the practiced and contextual knowledge. At the conceptualization level, we define the objectives of our system as well as the different tasks to accomplish while respecting of regulation, feedback and control constraints. At the operational level, our system is based on a hierarchical blackboard and multi-agent system which used to model semantic objects. Therefore, we consider the interpretation of images to not only one level, but to different granularities going from the macro level in term of the image sequence until the micro level in term of primitive, going back up toward a meso level which regroups several primitive for defining semantic objects. Indeed, our gait will be oriented objects multi-facet semantics taking account of radiometric, spatial, contextual and temporal dimension. Each Object being located in the scene can be considered as an evolutionary system that changes states. The spatio-temporal modelling has the tendency to replicate the evolution and the dynamics of objects. This can be represented by an automaton where states correlate to stages of the evolution of the object to identify. These stages constitute summits of the automaton and events that join these summits are represented by bows taking account the clock constraints. Therefore, the spatio-temporal modelling of the interpreted scene is a representation of objects models and translating their models of evolution.RENNES1-BU Sciences Philo (352382102) / SudocBREST-Télécom Bretagne (290192306) / SudocSudocFranceF

Fuzzy clustering based approach for ontology alignment

International audienceRecently, several ontologies have been proposed for real life domains, where these propositions are large and voluminous due to the complexity of the domain. Consequently, Ontology Aligning has been attracting a great deal of interest in order to establish interoperability between heterogeneous applications. Although, this research has been addressed, most of existing approaches do not well capture suitable correspondences when the size and structure vary vastly across ontologies. Addressing this issue, we propose in this paper a fuzzy clustering based alignment approach which consists on improving the ontological structure organization. The basic idea is to perform the fuzzy clustering technique over the ontology's concepts in order to create clusters of similar concepts with estimation of medoids and membership degrees. The uncertainty is due to the fact that a concept has multiple attributes so to be assigned to different classes simultaneously. Then, the ontologies are aligned bas (More

Hyperspectral image betweenness centrality clustering based adaptive spatial and spectral neighborhood approach for anomaly detection

International audienceSegmentation-based anomaly detectors proceeds to the clustering of the hyperspectral image as a first step. However, most of the well-known clustering methods cluster anomalous pixels as a part of the background. This paper presents a new hyperspectral image clustering approach based on the betweenness centrality measure. The proposed approach starts by the construction of an adaptive spatial and spectral neighborhood for each pixel. This neighborhood is based on the selection of the nearest spectral and spatial neighbors in multiple windows around each pixel to allow well-suited representation of the image features. In the next step, this neighborhood is clustered based on the edge betweenness measure algorithm that splits the image into regions sharing similar features. This approach (1) allows the reduction of intercluster relationship, (2) favors intracluster relations, and (3) preserves small clusters that can hold anomalous pixels. Experimental results show that the proposed approach is efficient for clustering and overcomes the state of the art approaches

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International audienceMedical association rules induction is used to discover useful correlations between pertinent concepts from large medical databases. Nevertheless, ARs algorithms produce huge amount of delivered rules and do not guarantee the usefulness and interestingness of the generated knowledge. To overcome this drawback, we propose an ontology based interestingness measure for ARs ranking. According to domain expert, the goal of the use of ARs is to discover implicit relationships between items of different categories such as ‘clinical features and disorders',‘clinical features and radiological observations', etc. That's to say, the itemsets which are composed of "similar" items are uninteresting. Therefore, the dissimilarity between the rule's items can be used to judge the interestingness of association rules; the more different are the items, the more interesting the rule is. In this paper, we design a distinct approach for ranking semantically interesting association rules involving the use of an ontology knowledge mining approach. The basic idea is to organize the ontology's concepts into a hierarchical structure of conceptual clusters of targeted subjects, where each cluster encapsulates "similar" concepts suggesting a specific category of the domain knowledge. The interestingness of association rules is, then, defined as the dissimilarity between corresponding clusters. That's to say, the further are the clusters of the items in the AR, the more interesting the rule is. We apply the method in our domain of interest - mammographic domain-using an existing mammographic ontology called Mammo*, with the goal of deriving interesting rules from past experiences, to discover implicit relationships between concepts modeling the domain

Ontology Knowledge Mining for Ontology Alignment

As the ontology alignment facilitates the knowledge exchange among the heterogeneous data sources, several methods have been introduced in literature. Nevertheless, few of them have been interested in decreasing the problem complexity and reducing the research space of correspondences between the input ontologies.This paper presents a new approach for ontology alignment based on the ontology knowledge mining. The latter consists on producing for each ontology a hierarchical structure of fuzzy conceptual clusters, where a concept can belong to several clusters simultaneously. Each level of the hierarchy reflects the knowledge granularity degree of the knowledge base in order to improve the effectiveness and speediness of the information retrieval. Actually, such method allows the knowledge granularity analyze between the ontologies and facilitates several ontology engineering techniques. The ontology alignment process is performed iteratively over the produced hierarchical structure of the fuzzy clusters using semantic techniques. Once the correspondent clusters are identified, we consider both syntactic and structural characteristics of their correspondent entities. The proposed approach has been tested over the OAEI benchmark dataset and some real mammographic ontologies since this work is a part of CMCU project for Mammographic images analysis for Assistance Diagnostic Breast Cancer. The system performs good results in the terms of precision and recall with respect to other alignment system

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International audienceAmong the most powerful tools for knowledge representation, we cite the ontology which allows knowledge structuring and sharing. In order to achieve efficient domain knowledge bases content, the latter has to establish well linked and knowledge between its components. In parallel, data mining techniques are used to discover hidden structures within large databases. In particular, association rules are used to discover co-occurrence relationships from past experiences. In this context, we propose, to develop a method to enrich existing ontologies with the identification of novel semantic relations between concepts in order to have a better coverage of the domain knowledge. The enrichment process is realized through discovered association rules. Nevertheless, this technique generates a large number of rules, where some of them, may be evident or already declared in the knowledge base. To this end, the generated association rules are categorized into three main classes: known knowledge, novel knowledge and unexpected rules. We demonstrate the applicability of this method using an existing mammographic ontology and patient's records

Combining Decision Fusion and Uncertainty Propagation to Improve Land Cover Change Prediction in Satellite Image Databases

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Toward a multi-temporal approach for satellite image interpretation

International audienceMulti-temporal remotely sensed images have proved to be of great interest for earth resource assessment and environment monitoring. Automatic or semi-automatic interpretation of these images becomes an important and complex task in computer vision for land use change detection. However, facing the complexity of such images, many classical image interpretation techniques become inefficient. In this paper, the proposed approach, based on multi-agent system and hierarchical blackboard architecture allows an intelligent, concise and flexible control of a multi-temporal scene interpretation. It proposes the combination of semantic network representing the generic description of the scene, and a state transition diagram, modeling the possible state transitions for each one of the classes of interest. This system produces a hierarchic description of the results as well as the structural context of the identified objects including the associated attributes. We illustrate the design and implementation of our system on a set of multi-temporal satellite images SPOT4 representing a center tunisian region for different dates in order to illustrate the potential of the proposed multi-temporal approach

A data mining based approach to predict spatiotemporal changes in satellite images

International audienceThe interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conclusions based on collected data a challenging task. Recently, data mining appears to be a promising research field leading to several interesting discoveries in various areas such as marketing, surveillance, fraud detection and scientific discovery. By integrating data mining and image interpretation techniques, accurate and relevant information (i.e. functional relation between observed parcels and a set of informational contents) can be automatically elicited.This study presents a new approach to predict spatiotemporal changes in satellite image databases. The proposed method exploits fuzzy sets and data mining concepts to build predictions and decisions for several remote sensing fields. It takes into account imperfections related to the spatiotemporal mining process in order to provide more accurate and reliable information about land cover changes in satellite images. The proposed approach is validated using SPOT images representing the Saint-Denis region, capital of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone