Semi-supervised hyperspectral unmixing

In this paper, an effective method is proposed that combines supervised and unsupervised unmixing. We assume a linear model for the hyperspectral data and incorporate information about endmembers that are known to be in the data into the model. This information can be acquired from a spectral library or extracted from the data. Utilizing a priori information can both improve the unmixing, and reduce the complexity of the problem. The method is quantitatively evaluated using simulated data and it is shown that the unmixing results improve and the computational time decreases when a priori information is used. The method is also applied on a real hyperspectral data set of an urban landscape. The estimated abundance maps improve when information about known endmembers is incorporated into the model.

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