Causal Markov Mesh Hierarchical Modeling for the Contextual Classification of Multiresolution Satellite Images

A. Montaldo, L. Fronda,I. Hedhli,G. Moser,S. B. Serpico,J. Zerubia

2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)(2019)

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摘要
In this paper, we address the problem of the joint classification of multiple images acquired on the same scene at different spatial resolutions. From an application viewpoint, this problem is of importance in several contexts, including, most remarkably, satellite and aerial imagery. From a methodological perspective, we use a probabilistic graphical approach and adopt a hierarchical Markov mesh framework that we have recently developed and models the spatial-contextual classification of multiresolution and possibly multisensor images. Here, we focus on the methodological properties of this framework. First, we prove the causality of the model, a highly desirable property with respect to the computational cost of the inference. Then, we prove the expression of the marginal posterior mode criterion for this model and discuss the related assumptions. Experimental results with multi-spectral and panchromatic satellite images are also presented.
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关键词
Multiresolution images, causality, hierarchical Markov random field, Markov mesh random field, semantic image segmentation
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