Figure 4

Interpretable matrix factorization for hyperspectral images. Each hyperspectral data cube is transformed into a dense matrix. Then, extreme components/signatures on all matrices are computed, using Simplex Volume Maximization. The final step includes the computation of the new representation of all signatures in a space, spanned by the extremes.