Factor graph models for multisensory data fusion: From low-level features to high level interpretation

A solution of difficult tasks in remotely sensed data information extraction can be reached by the development of more complex models. The most important step is in the selection of a relevant and universal methodology for data interpretation, classification, fusion, object detection, etc. Probabilistic graphical models [1] become a more and more popular way for image data annotation and classification [2, 3]. Factor graphs possess important properties such as probabilistic nature, explicit factorization properties, approximate inference, plausible inference of non-full data, easy augmenting, etc., and become relevant for the use in data interpretation systems. In this paper we present several applications of factor graphs for single/multisensory data fusion, classification, and an extension of the graph structure to extract landcover from unseen data. The application of factor graphs allow to obtain an improvement in data fusion/classification accuracy.