Les images satellites représentent de nos jours une source d’information incontournable. Elles sont exploitées dans diverses applications, telles que : la gestion des risques, l’aménagent des territoires, la cartographie du sol ainsi qu’une multitude d’autre taches. Nous exploitons dans cette thèse les Séries Temporelles d’Images Satellites (STIS) pour le suivi des évolutions des habitats naturels et semi-naturels. L’objectif est d’identifier, organiser et mettre en évidence des patrons d’évolution caractéristiques de ces zones.Nous proposons des méthodes d’analyse de STIS orientée objets, en opposition aux approches par pixel, qui exploitent des images satellites segmentées. Nous identifions d’abord les profils d’évolution des objets de la série. Ensuite, nous analysons ces profils en utilisant des méthodes d’apprentissage automatique. Afin d’identifier les profils d’évolution, nous explorons les objets de la série pour déterminer un sous-ensemble d’objets d’intérêt (entités spatio-temporelles/objets de référence). L’évolution de ces entités spatio-temporelles est ensuite illustrée en utilisant des graphes d’évolution.Afin d’analyser les graphes d’évolution, nous avons proposé trois contributions. La première contribution explore des STIS annuelles. Elle permet d’analyser les graphes d’évolution en utilisant des algorithmes de clustering, afin de regrouper les entités spatio-temporelles évoluant similairement. Dans la deuxième contribution, nous proposons une méthode d’analyse pluri-annuelle et multi-site. Nous explorons plusieurs sites d’étude qui sont décrits par des STIS pluri-annuelles. Nous utilisons des algorithmes de clustering afin d’identifier des similarités intra et inter-site. Dans la troisième contribution, nous introduisons une méthode d’analyse semi-supervisée basée sur du clustering par contraintes. Nous proposons une méthode de sélection de contraintes. Ces contraintes sont utilisées pour guider le processus de clustering et adapter le partitionnement aux besoins de l’utilisateur.Nous avons évalué nos travaux sur différents sites d’étude. Les résultats obtenus ont permis d’identifier des profils d’évolution types sur chaque site d’étude. En outre, nous avons aussi identifié des évolutions caractéristiques communes à plusieurs sites. Par ailleurs, la sélection de contraintes pour l’apprentissage semi-supervisé a permis d’identifier des entités profitables à l’algorithme de clustering. Ainsi, les partitionnements obtenus en utilisant l’apprentissage non supervisé ont été améliorés et adaptés aux besoins de l’utilisateur. / Nowadays, remotely sensed images constitute a rich source of information that can be leveraged to support several applications including risk prevention, land use planning, land cover classification and many other several tasks. In this thesis, Satellite Image Time Series (SITS) are analysed to depict the dynamic of natural and semi-natural habitats. The objective is to identify, organize and highlight the evolution patterns of these areas.We introduce an object-oriented method to analyse SITS that consider segmented satellites images. Firstly, we identify the evolution profiles of the objects in the time series. Then, we analyse these profiles using machine learning methods. To identify the evolution profiles, we explore all the objects to select a subset of objects (spatio-temporal entities/reference objects) to be tracked. The evolution of the selected spatio-temporal entities is described using evolution graphs.To analyse these evolution graphs, we introduced three contributions. The first contribution explores annual SITS. It analyses the evolution graphs using clustering algorithms, to identify similar evolutions among the spatio-temporal entities. In the second contribution, we perform a multi-annual cross-site analysis. We consider several study areas described by multi-annual SITS. We use the clustering algorithms to identify intra and inter-site similarities. In the third contribution, we introduce à semi-supervised method based on constrained clustering. We propose a method to select the constraints that will be used to guide the clustering and adapt the results to the user needs.Our contributions were evaluated on several study areas. The experimental results allow to pinpoint relevant landscape evolutions in each study sites. We also identify the common evolutions among the different sites. In addition, the constraint selection method proposed in the constrained clustering allows to identify relevant entities. Thus, the results obtained using the unsupervised learning were improved and adapted to meet the user needs.

<div>The proliferation of GPS-enabled devices has led to the development of numerous location-based services. These services need to process massive amounts of spatial data in real-time with high-throughput and low response time. The current scale of spatial data cannot be handled using centralized systems. This has led to the development of distributed spatial streaming systems. The performance of distributed streaming systems relies on how even the workload is distributed among their machines. However, the real-time streamed spatial data and query follow non-uniform spatial distributions that are continuously changing over time. Therefore, Distributed spatial streaming systems need to track the changes in the distribution of spatial data and queries and redistribute their workload accordingly. This thesis addresses the challenges of adapting to workload changes in distributed spatial streaming systems to improve the performance while preserving the system's security. </div><div>The thesis proposes TrioStat, an online workload estimation technique that relies on a probabilistic model for estimating the cost of partitions and machines of distributed spatial streaming systems. TrioStat has a decentralised technique to collect and maintain the required statistics in real-time with minimal overhead. In addition, this thesis introduces SWARM, a light-weight adaptive load-balancing protocol that continuously monitors the data and query workloads across the distributed processes of spatial data streaming systems, and redistribute the workloads soon as performance bottlenecks get detected. SWARM uses TrioStat to estimate the workload of the system's machines. Although using adaptive load-balancing techniques significantly improves the performance of distributed streaming systems, they make the system vulnerable to attacks. In this thesis, we introduce a novel attack model that targets adaptive load-balancing mechanisms of distributed streaming systems. The attack reduces the throughput and the availability of the system by making it stay in a continuous state of rebalancing. The thesis proposes Guard, a component that detects and blocks attacks that target the adaptive load balancing of distributed streaming systems. Guard is deployed in SWARM to develop an attack-resilient adaptive load balancing mechanism for Distributed spatial streaming systems.<br></div>