New informed linear mixing model and nmf-based unmixing method addressing spectral variability with an application to mineral detection and mapping using prisma hyperspectral remote sensing data

In these investigations, a new informed linear mixing model addressing spectral variability with an associated informed penalized hyperspectral unmixing algorithm is first proposed. This algorithm, which optimizes a new cost function with original iterative and multiplicative update rules, is based on nonnegative matrix factorization. Then, this algorithm is used for detecting and mapping several mineral deposits in the Algerian Central Hoggar. This method unmixes the considered PRISMA hyperspectral remote sensing data by exploiting known spectra of some minerals of interest. Obtained abundance fraction maps are then used to establish a classification map of the investigated area, with the considered mineral deposit classes, at a finer spatial resolution.