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International audience; In this paper, the problem of the classification of multireso-lution and multisensor remotely sensed data is addressed by proposing a multiscale Markov mesh model. Multiresolution and multisensor fusion are jointly achieved through an explicitly hierarchical probabilistic graphical classifier, which uses a quadtree structure to model the interactions across different spatial resolutions, and a symmetric Markov mesh random field to deal with contextual information at each scale and favor applicability to very high resolution imagery. Differently from previous hierarchical Markovian approaches, here, data collected by distinct sensors are fused through either the graph topology itself (across its layers) or decision tree ensemble methods (within each layer). The proposed model allows taking benefit of strong analytical properties, most remarkably causality, which make it possible to apply time-efficient non-iterative inference algorithms.

International audience; In this paper, we propose a novel hierarchical method for remote sensing image classification. The proposed approach integrates an explicit hierarchical graph-based classifier, which uses a quad-tree structure to model multiscale interactions, and a third order Markov mesh random field to deal with pixel wise contextual information in the same scale. The choice of a quad-tree and the third order Markov mesh allow taking benefit from their good analytical properties (especially causality) and consequently apply non-iterative algorithms. Indeed, the Markov mesh is used to incorporate spatial information in each scale of the quad-tree while keeping the causality of the hierarchical model. 1. INTRODUCTION Nowadays, a substantial amount and variety of last-generation high resolution (HR) satellite missions provide images acquired simultaneously at different spatial and spectral resolutions. The main difficulty is to develop a classifier that jointly utilizes the benefits of multi-band and multi-resolution input data while maintaining a good tradeoff between accuracy and computation time. Classification technique as an example of inverse problems can be regarded as the process that estimates hidden information (or latent variables) í µí±¥ (i.e., land cover class labels) from observations í µí±¦ (i.e., satellite data) attached to a set of nodes S. Hence, this problem is ill posed in the sense of Hadamard [1]. The classical way to handle this disadvantage is to regularize the solution by imposing prior knowledge on the labels, which may include, most remarkably, spatial-contextual priors. In this framework, Markov random field (MRF) models are widely used in image classification since they provide a convenient and consistent way of integrating contextual information into the classification scheme [2, 5]. Because of their generally non-causal nature, MRF models for classification lead to iterative inference algorithms that are computationally demanding [6-10]. By contrast, MRF models defined according to hierarchical structures exhibit good methodological and application-oriented properties including causality by using appropriate graphs [11]. Indeed, from a random process perspective, Markovian models show