Decision Fusion for Hyperspectral Classification

Abstract

In the recent years, pixel-wise classification of hyperspectral images aroused many developments, and the literature now provides various classifiers for numerous applications. In this chapter, we present a generic framework where the redundant or complementary results provided by multiple classifiers can actually be aggregated. Taking advantage from the specificities of each classifier, the decision fusion thus increases the overall classification performances. 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. The general framework proposed for combining information from several individual classifiers in multiclass classification 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 fusion ruled by these two accuracy measures. The method is illustrated by considering the classification of hyperspectral remote sensing images from urban areas. It is tested and validated with two classifiers on a ROSIS image from Pavia, Italy. 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 standard fuzzy fusion schemes.