12 September 2007 Statistics-based endmember extraction algorithms for hyperspectral imagery
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Endmember extraction has received considerable interest in recent years. Many algorithms have been developed for this purpose and most of them are designed based on convexity geometry such as vertex or endpoint projection and maximization of simplex volume. This paper develops statistics-based approaches to endmember extraction in the sense that different orders of statistics are used as criteria to extract endmembers. The idea behind the proposed statistics-based endmember extraction algorithms (EEAs) is to assume that a set of endmmembers constitute the most un-correlated sample pool among all the same number of signatures with correlation measured by statistics which include variance specified by 2nd order statistics, least squares error (LSE) also specified by 2nd order statistics (variance), 3rd order statistics (skewness), 4th order statistics (kurtosis), kth moment, entropy specified by infinite order of statistics and statistical independency measured by mutual information. Of particular interest are Independent Component Analysis-based EEAs which use statistics of various orders such as variance, skewness, kurtosis the kth moment and infinite orders including entropy and divergence. In order to substantiate proposed statistics-based EEAs, experiments using synthetic and real images are conducted in comparison with several popular and well-known EEAs such as Pixel Purity Index (PPI), N-finder algorithm (N-FINDR).
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Shih-Yu Chu, Shih-Yu Chu, Chao-Cheng Wu, Chao-Cheng Wu, Chein-I Chang, Chein-I Chang, "Statistics-based endmember extraction algorithms for hyperspectral imagery", Proc. SPIE 6661, Imaging Spectrometry XII, 66610F (12 September 2007); doi: 10.1117/12.732606; https://doi.org/10.1117/12.732606

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