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In this letter, an eigenvalue-based empirical method is proposed in order to estimate the number of endmembers in hyperspectral data. This method is based on the distribution of the differences of the eigenvalues from the correlation and the covariance matrices, respectively. The eigenvalues corresponding to the noise are identical in the covariance and the correlation matrices, while the eigenvalues corresponding to the signal (the endmembers) are larger in the correlation matrix than in the covariance matrix. The proposed method is totally parameter free and very fast. It is validated by experiments carried on both synthetic and real data sets.