Spectral unmixing using nonnegative basis learning: comparison of geometrical and statistical endmember extraction algorithms

被引：0

作者：

Parente, Mario ^{[1
]}

机构：

[1] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA

来源：

SPACE EXPLORATION TECHNOLOGIES | 2008年 / 6960卷

关键词：

endmember extraction; CRISM; spectral unmixing; clustering; NMF; GMVQ;

D O I：

10.1117/12.777895

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) data can be used to identify the presence of minerals on the surface or Mars. The data are a peculiar set from which to extract endmembers. Using an image from a previously investigate area of the surface, we compare a geometrical and a statistical algorithm for extracting endmembers for mineral identification. Both algorithms correctly identified the spectra of the two minerals known to be present in the Nili Fossae region of Mars. Both algorithms suffer from linearity assumption. Even though the statistical algorithm is less robust with respect to outliers, it has potential to extract endmembers in complex data clouds because of its local nature.

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页数：13

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