On the unmixing of MEx/OMEGA hyperspectral data
نویسنده
چکیده
This article presents a comparative study of three different types of estimators used for supervised linear unmixing of a MEx/OMEGA hyperspectral cube. The algorithms take into account the constraints of the abundance fractions, so as to get physically interpretable results. Abundance and spatial reconstruction error maps show that using a Bayesian MAP estimator, a satisfying compromise between complexity and performance can be achieved.
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تاریخ انتشار 2010