Decision Fusion on Supervised and Unsupervised Classifiers for Hyperspectral Imagery

Abstract

A decision fusion approach is developed to combine the results from supervised and unsupervised classifiers. The final output takes advantage of the power of a support-vector-machine-based supervised classification in class separation and the capability of an unsupervised classifier, such as K -means clustering, in reducing trivial spectral variation impact in homogeneous regions. This approach can simply adopt the majority voting (MV) rule to achieve the same objective of object-based classification. In this letter, we propose a weighted MV (WMV) rule for decision fusion, where pixels in the same segment contribute differently according to their distance to the spectral centroid. The WMV rule can further improve the performance of the original MV rule. A series of unsupervised classifiers is investigated in the use of decision fusion, and recommendations are provided on the best unsupervised classifiers to be selected.