Weighted decision fusion for supervised and unsupervised hyperspectral image classification

Abstract

A decision fusion approach is proposed to combine the results from supervised and unsupervised classifiers. The final output takes advantage of the power of supervised classification in class separation and the capability of unsupervised classification in reducing spectral variation impact in homogeneous regions. This approach simply adopts the majority voting rule, but can achieve the same objective of object-based classification. In this paper, we propose a weighted majority voting rule for decision fusion, where pixels in the same segment contribute differently according to their distance to the spectral centroid. The weighted majority voting rule can further improve the performance of the majority voting rule.