The effect of classifier agreement on the accuracy of the combined classifier in decision level fusion

Abstract

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. 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.