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- W2020285892 abstract "Endmember extraction has received considerable interest in recent years. Many algorithms have been developed for thispurpose and most of them are designed based on convexity geometry such as vertex or endpoint projection andmaximization of simplex volume. This paper develops statistics-based approaches to endmember extraction in the sensethat different orders of statistics are used as criteria to extract endmembers. The idea behind the proposed statistics-basedendmember extraction algorithms (EEAs) is to assume that a set of endmmembers constitute the most un-correlatedsample pool among all the same number of signatures with correlation measured by statistics which include variancespecified by 2nd order statistics, least squares error (LSE) also specified by 2nd order statistics, skewness 3rd orderstatistics, kurtosis 4th order statistics, k th moment and statistical independency specified by infinite order of statisticsmeasured by mutual information. In order to substantiate proposed statistics-based EEAs, experiments using syntheticand real images are conducted for demonstration." @default.
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- W2020285892 date "2008-04-03" @default.
- W2020285892 modified "2023-09-23" @default.
- W2020285892 title "High-order statistics-based approaches to endmember extraction for hyperspectral imagery" @default.
- W2020285892 doi "https://doi.org/10.1117/12.777725" @default.
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