Geometric unmixing of large hyperspectral images: a barycentric coordinate approach

Jutten, “Blind hyperspectral unmixing using an extended linear mixing model to address spectral variability,” IEEE Transactions on Image Processing, vol.. McLaughlin,

The paper reviews an algorithm for the classification of landscape elements and identification of small objects in hyperspectral images by the method of

In order to satisfy criterion (4), in NIPALS, PLS-R and new Mode A PLS-PM, when work- ing on standardized variables, optimal weights are calculated as Pearson’s

Using a three bar truss didactic example we identified the following conditions under which the basic least squares method is systematically outperformed by the Bayesian

The the- oretical convergence rates of the interpolation and the methods for the approximation of derivatives, integrals and antiderivatives derived from the rational functions

Numerous authors have derived results about the Lebesgue function and constant associated with Lagrange interpolation at various kinds of nodes; see [5, 6, 18] and the

The non-negative CP decomposition could be understood as an extension of the linear unmixing of 2-way (spatial and spectral) hyperspectral data [6] to the multi-linear un- mixing

Isoperimetric inequality, quantitative isoperimetric inequality, isoperimetric deficit, barycentric asymmetry, shape derivative, optimality