Noise-adjusted non orthogonal linear projections for hyperspectral data analysis

Independent Component Analysis (ICA) and Projection Pursuit (PP) are non orthogonal linear projection methods useful for dimensionality reduction of hyperspectral data cubes, in many cases more interesting than the standard Principal Component Analysis (PCA) but unfortunately not very robust to the noise. In this paper, the spatial correlation information is taken into account in order to improve the performances of both methods, following the ideas behind the so-called Noise-Adjusted Principal Component Analysis (NAPCA). This leads to the construction of two robust non orthogonal linear projection methods, respectively called Noise-Adjusted Independant Component Analysis (NAICA) and Noise-adjusted Projection Pursuit (NAPP).