24 October 2007 Information sources fusion approach in forest stand classification
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The classification of remote-sensing images based on multiple information sources offers a consistent method for the automatic cartography of forest stands. However, fusion models reveal problems of combinatorial explosion due to the calculation of the assignment functions. This article proposes an information-fusion approach that responds to the need for updating the forest inventory, based on belief theory. It illustrates a solution that overcomes the problem of combinatorial explosion that arises with the evaluation of evidence-mass functions which are used as the frame of discernment events. This solution is based on a refinement of the frame of discernment based on the determination of all focal elements (singleton or composite hypothesis of non null masses). Thus, the combination of information source masses would involve only the focal elements masses. In the approach proposed here, the notions of fuzzy logic and possibility theory have been used for the calculation of masses and combinations between classes as an intermediary phase in arriving at belief functions. The result of the application of our fusion approach revealed a significant improvement in optimizing the calculation of mass evidence functions and thus achieving a satisfactory classification.
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Zouhour Ben Dhiaf, Jacky Desachy, Atef Hamouda, "Information sources fusion approach in forest stand classification", Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480V (24 October 2007); doi: 10.1117/12.738178; https://doi.org/10.1117/12.738178

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