Application of nonnegative principal component analysis in hyperspectral imaging

Peter Bajorski

doi:10.1117/12.677375

1 September 2006 Application of nonnegative principal component analysis in hyperspectral imaging

Peter Bajorski

Proceedings Volume 6302, Imaging Spectrometry XI; 63020G (2006) https://doi.org/10.1117/12.677375
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States

Abstract

The classic PCA (Principal Component Analysis) has been applied in hyperspectral imaging with varying success. One obstacle in its application is the potential physical interpretation of the principal components, which is questionable unless the principal component coefficients are nonnegative. In this paper, we show hyperspectral imaging applications of a recently developed methodology of nonnegative PCA, which overcomes this difficulty by constructing nonnegative principal components. We construct an approximation of a physics-derived target space, and suggest some interpretations of the resulting components.

Citation Download Citation

Peter Bajorski "Application of nonnegative principal component analysis in hyperspectral imaging", Proc. SPIE 6302, Imaging Spectrometry XI, 63020G (1 September 2006); https://doi.org/10.1117/12.677375

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