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Generalized fusion: a new framework for hyperspectral detection

The purpose of this paper is to introduce a general type of detection fusion that allows combining a set of basic detectors into one, more versatile, detector. The fusion can be performed based on the spectral information contained in a pixel, global characteristics of the background and target spaces, as well as spatial local information. The new approach shown in this paper is especially promising in the context of recent geometric and topological approaches that produce complex structures for the background and target spaces. We show specific examples of generalized fusion and present some results on false alarm rates and probabilities of detection of fused detectors. We show that continuum fusion is a special case of generalized fusion. Our new framework allows better understanding of continuum fusion, as well as other useful types of fusion, such as discrete fusion proposed in this paper. We also explain the relationship between the generalized likelihood-ratio detectors and various fusion detectors.