Abstract
Decision level fusion, using a specific criterion or algorithm to integrate the classified results from different classifiers, has shown great benefits to improve classification accuracy of multi-source remote sensing images. In this paper, three decision level fusion methods and four schemes for input data are used to hyperspectral remote sensing image classification. Different feature combination and decision level fusion approaches are experimented and analyzed, and the results show that decision level fusion is effective to improve the performance of hyperspectral remote sensing image classification.
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