Community Research and Development Information Service - CORDIS


Recently, decision level fusion has shown great potential to increase classification accuracy beyond the level reached by individual classifiers. A considerable body of literature exists on identifying optimal ways to combine classifiers. However, the selection of the classifiers to be combined is equally, if not more, crucial if an improvement is to be made for certain classifier combination schemes. Agreement among classifiers can inhibit the gains obtained regardless of the method used to combine them. In this work, the level of agreement between different classifiers used in remote sensing is assessed based on statistical measures. A study is performed in which an image is classified by several methods with different degrees of agreement between them. The results are then combined using decision fusion schemes and the increase of accuracy is observed for each combination of the individual classifications.

Additional information

Authors: PETRAKOS M, Liaison Systems S.A., Athens (GR);BENEDIKTSSON J A, University of Iceland, Reykjavik (IS);KANELLOPOULOS I, European Commission, Joint Research Centre, Institute for Environment and Sustainability, Ispra (IT)
Bibliographic Reference: An article published in: IEEE Transactions on Geoscience and Remote Sensing, Volume 39 Issue 11, Nov. 2001, pp. 2539 -2546.
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