A New Linear Mixture Model for Hyperspectral Image Analysis

In the original linear mixture model, the same set of endmembers is used for mixture analysis of an entire image. Since not all of these endmembers participate in the mixing process of each pixel, it is more reasonable to find a subset of endmembers that is actually involved in the construction of each pixel. The resulting mixture model, referred to as multiple endmember spectral mixture analysis (MESMA), has been proposed. In this paper, we develop two algorithms to determine the optimal set of endmembers for each pixel, where the sum-to-one and non-negativity constraints can be automatically relaxed. We believe these algorithms can help to improve the accuracy of linear mixture analysis of hyperspectral imagery; it is also useful to multispectral imagery to overcome the limitation due to low data dimensionality.