A metrological spectral difference space for the statistical modelling of hyperspectral images

Answering to metrological constraints typically required in the context of industrial and medical applications, a spectral difference space is introduced in this work. In this space, an acquired hyperspectral data is treated as measurements. Then, modelling the spectral difference space as multivariate Normal laws, a Gaussian mixture model is used in a classification task of remote sensing images. An encouraging result is obtained, comparing the proposed space with a data-driven one. Moreover, it offers a starting point in developing a directly interpretable spectral analysis tools.