Unsupervised nonlinear spectral unmixing by means of NLPCA applied to hyperspectral imagery
In the literature, for sake of simplicity it is usually assumed that the model ruling spectral mixture in a hyperspectral pixels is basically linear. However, in many real life cases the different materials are usually in intimate association, like sand grains, resulting in a nonlinear mixture. Unfortunately, modeling a nonlinear approach is not trivial, and a general procedure is still up to be found. Aim of this paper is to evaluate the potentialities of Nonlinear Principal Component Analysis (NLPCA) as an approach to perform a nonlinear unmixing for the unsupervised extraction and quantification of the end-members. From this point of view scope of this paper is to demonstrate that the NLPCs derived from the proposed process can be considered as end-members. To perform an accurate evaluation, the proposed algorithm has been tested on two different hyperspectral datasets and compared with other approaches found in the literature.