Apprentissage de connaissances structurelles pour la classification automatique d’images satellitaires dans un environnement amazonien

Classical methods for satellite image analysis appear inadequate for the current bulky data flow. Thus, makingthe interpretation of such images automatic becomes crucial for the analysis and management of phenomenachanging in time and space, observable by satellite. Consequently, this work aims to contribute to the dyna-mic land cover cartography from satellite images, by expressive and easily interpretable mechanisms, and byexplicitly taking into account structural aspects of geographic information. It is part of the object-based imageanalysis framework, and assumes that it is possible to extract useful contextual knowledge from existing maps.Thus, a supervised parameterization method of an image segmentation algorithm is proposed, taking a seg-mentation derived from a land cover map as reference. Secondly, a supervised classification of geographicalobjects is presented. It combines machine learning by Inductive Logic Programming and the Multi-class RuleSet Intersection approach. Finally, prediction confidence indexes are defined to assist interpretation. These ap-proaches are applied to the French Guiana coastline cartography. The results demonstrate the feasibility ofthe segmentation parameterization, but also its variability as a function of the reference map classes and ofthe input data. Nevertheless, methodological developments allow to consider an operational implementation ofsuch an approach. The results concerning the object supervised classification show that it is possible to induceexpressive classification rules that convey consistent and structural information in a given application contextand lead to reliable predictions, with overall accuracy and Kappa values equal to, respectively, 84.6% and 0.7.In conclusion, this work contributes to the automation of the dynamic cartography from remotely sensed imagesand proposes original and promising perspectives.Les méthodes actuelles d'analyse et d'interprétation d'images satellitaires s'avèrent inadaptées au volume du flux actuel et futur des données. L'automatisation de l'interprétation contextuelle de ces images devient donc cruciale pour la caractérisation, le suivi, la modélisation et la prédiction des phénomènes observables par satellite et évoluant dans le temps et l'espace. Dans ce contexte, ce travail vise à contribuer à la cartographie dynamique de l'occupation/usage du sol à partir d'images satellitaires, par des mécanismes expressifs, facilement interprétables et faisant intervenir explicitement les aspects structurels de l'information géographique. Il s'inscrit dans le cadre de l'analyse d'images basée objet et fait l'hypothèse qu'il est possible d'extraire les connaissances contextuelles utiles à partir de cartes existantes.Ainsi, une méthode de paramétrage supervisé d'un algorithme de segmentation d'images, à partir d'une segmentation de référence fournie par une carte d'occupation du sol, est proposée. Dans un deuxième temps, une méthode de classification supervisée d'objets géographiques est présentée, combinant apprentissage automatique à partir de cartes, par Programmation Logique Inductive (PLI), et classement par l'approche Multi-class Rule Set Intersection (MRSI). Enfin, des indices de confiance de prédiction sont définis, facilitant l'interprétation et l'acceptabilité des résultats par l'utilisateur final.Ces approches sont évaluées et discutées dans deux contextes applicatifs relatifs à la cartographie de la bande côtière guyanaise. Les résultats démontrent la faisabilité du paramétrage de la segmentation, mais également la variabilité des valeurs optimales du paramètre en fonction des classes de la nomenclature de la carte de référence et des données d'entrée du processus de paramétrage. Des développements méthodologiques permettent cependant d'envisager une mise en oeuvre opérationnelle de la méthode. Les résultats de la classification supervisée montrent, quant à eux, qu'il est possible d'induire des règles de classification expressives, véhiculant des informations cohérentes et structurelles dans un contexte applicatif donné, et conduisant à des valeurs satisfaisantes de précision globale et de Kappa (respectivement 84,6% et 0,7).Ce travail de thèse contribue ainsi à l'automatisation de la cartographie dynamique à partir d'image de télédétection et propose des perspectives originales et prometteuses

Structural knowledge learning from maps for supervised land cover/use classification: Application to the monitoring of land cover/use maps in French Guiana

International audienceThe number of satellites and sensors devoted to Earth observation has become increasingly elevated, delivering extensive data, especially images. At the same time, the access to such data and the tools needed to process them has considerably improved. In the presence of such data flow, we need automatic image interpretation methods, especially when it comes to the monitoring and prediction of environmental and societal changes in highly dynamic socio-environmental contexts. This could be accomplished via artificial intelligence. The concept described here relies on the induction of classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system, combined with a multi-class classification procedure. This methodology was used to monitor changes in land cover/use of the French Guiana coastline. One hundred and fifty-eight classification rules were induced from 3 diachronic land cover/use maps including 38 classes. These rules were expressed in first order logic language, which makes them easily understandable by non-experts. A 10-fold cross-validation gave significant average values of 84.62%, 99.57% and 77.22% for classification accuracy, specificity and sensitivity, respectively. Our methodology could be beneficial to automatically classify new objects and to facilitate object-based classification procedures

Apprentissage de connaissances structurelles à partir de cartes et classification multi-classes : Application à la mise a jour de cartes d'occupation du sol

International audienceLe nombre de satellites et de capteurs pour la télédétection dédiés à l'observation de la Terre ne cesse d'augmenter, permettant ainsi d'avoir une masse de données importante en particulier en matière d'images. Parallèlement, un effort permanent vise, d'une part, à améliorer l'accès à ces données et, d'autre part, à développer d'avantages d'outils pour les manipuler. De tels efforts sont particulièrement utiles dans des contextes socio-environnementaux très dynamiques spatialement et temporellement, pour lesquels il est nécessaire de suivre et de prédire les événements environnementaux et sociétaux. En revanche, en présence d'un tel flux de données, nous avons besoin de méthodes automatiques d'interprétation d'images. Une solution envisageable pour répondre à ce besoin est de bénéficier des atouts de l'intelligence artificielle pour l'obtention de cartes d'occupation du sol issues d'une classification des régions des images. Afin de contribuer à l'automatisation de la classification, nous proposons une méthode d'induction de règles interprétables par des non-experts et mettant en évidence, explicitement, des connaissances structurelles. Cette méthode s'appuie sur la programmation logique inductive (PLI) et en particulier sur le système inductif ”Aleph”. L'application de la méthode de classification Multi-class Rule Set Intersection (MRSI) permet ensuite de classifier tout nouvel objet au regard des ses caractéristiques intrinsèques et de celles des objets environnants. Nous avons appliqué notre méthodologie à l'étude de la dynamique du littoral de la Guyane Française. Suite à ce travail, nous avons induit 136 règles de classification pour 38 classes d'occupation du sol. Ces règles sont intelligibles et simples à interpréter de par l'utilisation de la logique du premier ordre. Les performances du système ont été évaluées par la validation croisée. En moyenne, la précision, la spécificité et la sensibilité sont, respectivement, égales à 84,62%, 99,57% et 77,22%. Ces résultats quantitatifs montrent une bonne performance de la méthodologie pour la mise à jour automatique de cartes d'occupation du sol et/ou l'assistance aux opérateurs utilisant l'analyse d'image orientée-objet

Automatic learning of structural knowledge from geographic information for updating land cover maps

International audienceThe number of satellites and remote sensing sensors devoted to earth observation becomes increasingly high, providing more and more data and especially images. In the same time the access to such a data and to the tools to process them has been considerably improved. In the presence of such data flow - and regarding the necessity to follow up and predict environmental and societal changes in highly dynamic socio-environmental contexts - we need automatic image interpretation methods. This could be accomplished by exploring some strengths of artificial intelligence. Our main idea consists in inducing classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system. We applied our proposed methodology to three land cover/use maps of the French Guiana littoral. One hundred and forty six classification rules were induced for the 39 land-cover classes of the maps. These rules are expressed in first order logic language which make them intelligible and interpretable by non-experts. A ten-fold cross validation gave average values for classification accuracy, specificity and sensibility equal to, respectively, 98.82 %, 99.65% and 70%. The proposed methodology could be valuably exploited to automatically classify new objects and/or help operators using object-based classification procedures

Structurel Knowledge learning from satellite images and exogenous data for dynamic mapping of the amazonian environment

Les méthodes classiques d'analyse d'images satellites sont inadaptées au volume actuel du flux de données. L'automatisation de l'interprétation de ces images devient donc cruciale pour l'analyse et la gestion des phénomènes observables par satellite et évoluant dans le temps et l'espace. Ce travail vise à automatiser la cartographie dynamique de l'occupation du sol à partir d'images satellites, par des mécanismes expressifs, facilement interprétables en prenant en compte les aspects structurels de l'information géographique. Il s'inscrit dans le cadre de l'analyse d'images basée objet. Ainsi, un paramétrage supervisé d'un algorithme de segmentation d'images est proposé. Dans un deuxième temps, une méthode de classification supervisée d'objets géographiques est présentée combinant apprentissage automatique par programmation logique inductive et classement par l'approche multi-class rule set intersection. Ces approches sont appliquées à la cartographie de la bande côtière Guyanaise. Les résultats démontrent la faisabilité du paramétrage de la segmentation, mais également sa variabilité en fonction des classes de la carte de référence et des données d'entrée. Les résultats de la classification supervisée montrent qu'il est possible d'induire des règles de classification expressives, véhiculant des informations cohérentes et structurelles dans un contexte applicatif donnée et conduisant à des valeurs satisfaisantes de précision et de KAPPA (respectivement 84,6% et 0,7). Ce travail de thèse contribue ainsi à l'automatisation de la cartographie dynamique à partir d'images de télédétection et propose des perspectives originales et prometteuses.Classical methods for satellite image analysis are inadequate for the current bulky data flow. Thus, automate the interpretation of such images becomes crucial for the analysis and management of phenomena changing in time and space, observable by satellite. Thus, this work aims at automating land cover cartography from satellite images, by expressive and easily interpretable mechanism, and by explicitly taking into account structural aspects of geographic information. It is part of the object-based image analysis framework, and assumes that it is possible to extract useful contextual knowledge from maps. Thus, a supervised parameterization methods of a segmentation algorithm is proposed. Secondly, a supervised classification of geographical objects is presented. It combines machine learning by inductive logic programming and the multi-class rule set intersection approach. These approaches are applied to the French Guiana coastline cartography. The results demonstrate the feasibility of the segmentation parameterization, but also its variability as a function of the reference map classes and of the input data. Yet, methodological developments allow to consider an operational implementation of such an approach. The results of the object supervised classification show that it is possible to induce expressive classification rules that convey consistent and structural information in a given application context and lead to reliable predictions, with overall accuracy and Kappa values equal to, respectively, 84,6% and 0,7. In conclusion, this work contributes to the automation of the dynamic cartography from remotely sensed images and proposes original and promising perpective

A Kolmogorov complexity view of analogy : From logical modeling to experimentations

PRichBayo001International audienceAnalogical reasoning is considered as one of the main mechanisms underlying human intelligence and creativity, allowing the paradigm shift essential to a creative process. More specific is the notion of analogical proportion like “2 is to 4 as 5 is to 10” or “read is to reader as lecture is to lecturer”: such statements can be precisely described within an algebraic framework. When the proportion holds between concepts as in “engine is to car as heart is to human” or “wine is to France as beer is to England”, applying an algebraic framework is less straightforward and a new way to understand analogical proportions on the basis of Kolmogorov complexity theory may seem more appropriate. This viewpoint has been used to develop a classifier detecting analogies in natural language. Despite their apparent difference, it is quite clear that the two viewpoints should be strongly related. In this paper, we investigate the link between a purely abstract view of analogical proportions and a definition based on Kolmogorov complexity theory. This theory is used as a backbone to experiment a classifier of natural language analogies whose results are consistent with the abstract setting

Evaluation of analogical proportions through Kolmogorov complexity

International audienceIn this paper, we try to identify analogical proportions, i.e., statements of the form “a is to b as c is to d”, expressed in linguistic terms. While it is conceivable to use an algebraic model for testing proportions such as “2 is to 4 as 5 is to 10”, or even such as “read is to reader as lecture is to lecturer”, there is no algebraic framework to support statements such as “engine is to car as heart is to human” or “wine is to France as beer is to England”, helping to recognize them as meaningful analogical proportions. The idea is then to rely on text corpora, or even on the Web itself, where one may expect to find the pragmatics and the semantics of the words, in their common use. In that context, in order to attach a numerical value to the “analogical ratio” corresponding to the phrase “a is to b”, we start from the works of Kolmogorov on complexity theory. This is the basis for a universal measure of the information content of a word a, or of a word a with respect to another one b, which, in practice, is estimated in a statistical manner. We investigate the link between a purely logical, recently introduced view of analogical proportions and its counterpart based on Kolmogorov theory. The criteria proposed for testing candidate proportions fit with the expected properties (symmetry, central permutation) of analogical proportions. This leads to a new computational method to define, and ultimately to try to detect, analogical proportions in natural language. Experiments with classifiers based on these ideas are reported, and results are rather encouraging with respect to the recognition of common sense linguistic analogies. The approach is also compared with existing works on similar problems

Automatic learning of structural knowledge from geographic information for updating land cover maps

International audienceThe number of satellites and remote sensing sensors devoted to earth observation becomes increasingly high, providing more and more data and especially images. In the same time the access to such a data and to the tools to process them has been considerably improved. In the presence of such data flow - and regarding the necessity to follow up and predict environmental and societal changes in highly dynamic socio-environmental contexts - we need automatic image interpretation methods. This could be accomplished by exploring some strengths of artificial intelligence. Our main idea consists in inducing classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system. We applied our proposed methodology to three land cover/use maps of the French Guiana littoral. One hundred and forty six classification rules were induced for the 39 land-cover classes of the maps. These rules are expressed in first order logic language which make them intelligible and interpretable by non-experts. A ten-fold cross validation gave average values for classification accuracy, specificity and sensibility equal to, respectively, 98.82 %, 99.65% and 70%. The proposed methodology could be valuably exploited to automatically classify new objects and/or help operators using object-based classification procedures