Nonnegative principal components for hyperspectral imaging

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 an AVIRIS, and suggest some interpretations of the resulting components.