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In this letter, the supervised classification algorithm support vector machines is extended to map both pure pixels and mixed pixels using hyperspectral data. The margins between the hyperplanes formed by the pixels on the class boundaries are recognized as mixed region, and the space beyond this region is related to pure pixels. In this way, each endmember is modeled by a set of training samples instead of a single (representative) spectrum to accommodate the variations within the relative pure pixels due to system noise. Unmixing outputs generate an integrated soft- and hard-classification map. The better performance comparing with conventional spectral unmixing method was demonstrated using hyperspectral data sets.