Unsupervised nonlinear spectral unmixing by means of NLPCA applied to hyperspectral imagery

Abstract

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.