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

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3 Author(s)
Aliaksei Makarau ; Photogrammetry and Image Analysis, Remote Sensing Technology Institute, German Aerospace Center DLR, Oberpfaffenhofen, D-82234 Wessling, Germany ; Gintautas Palubinskas ; Peter Reinartz

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.

Published in:

2012 IEEE International Geoscience and Remote Sensing Symposium

Date of Conference:

22-27 July 2012