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Empirical Automatic Estimation of the Number of Endmembers in Hyperspectral Images

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4 Author(s)
Bin Luo ; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China ; Jocelyn Chanussot ; Sylvain Doute ; Liangpei Zhang

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.

Published in:

IEEE Geoscience and Remote Sensing Letters  (Volume:10 ,  Issue: 1 )