Decision Fusion for the Classification of Urban Remote Sensing Images
Abstract
The classification of very high-resolution remote sensing images from urban areas is addressed by considering the fusion of multiple classifiers which provide redundant or complementary results. The proposed fusion approach is in two steps. In a first step, data are processed by each classifier separately and the algorithms provide for each pixel membership degrees for the considered classes. Then, in a second step, a fuzzy decision rule is used to aggregate the results provided by the algorithms according to the classifiers' capabilities. In this paper, a general framework for combining information from several individual classifiers in multiclass classification is proposed. It is based on the definition of two measures of accuracy. The first one is a point-wise measure which estimates for each pixel the reliability of the information provided by each classifier. By modeling the output of a classifier as a fuzzy set, this point-wise reliability is defined as the degree of uncertainty of the fuzzy set. The second measure estimates the global accuracy of each classifier. It is defined a priori by the user. Finally, the results are aggregated with an adaptive fuzzy operator ruled by these two accuracy measures. The method is tested and validated with two classifiers on IKONOS images from urban areas. The proposed method improves the classification results when compared with the separate use of the different classifiers. The approach is also compared to several other fuzzy fusion schemes.
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