Stability of Dimensionality Reduction Methods Applied on Artificial Hyperspectral Images

摘要

Dimensionality reduction is a big challenge in many areas. In this research we address the problem of high-dimensional hyperspectral images in which we are aiming to preserve its information quality. This paper introduces a study stability of the non parametric and unsupervised methods of projection and of bands selection used in dimensionality reduction of different noise levels determined with different numbers of data points. The quality criteria based on the norm and correlation are employed obtaining a good preservation of these artificial data in the reduced dimensions. The added value of these criteria can be illustrated in the evaluation of the reduction's performance, when considering the stability of two categories of bands selection methods and projection methods. The performances of the method are verified on artificial data sets for validation. An hybridization for a better stability is proposed in this paper, Band Clustering (BandClust) with Multidimensional Scaling (MDS) for dimensionality reduction. Examples are given to demonstrate the hybridization originality and relevance(BandClust/MDS) of the analysis carried out in this paper.