Title :
Noise-adjusted non orthogonal linear projections for hyperspectral data analysis
Author :
Lennon, M. ; Mercier, G.
Author_Institution :
Ecole Nat. Superieure des Telecommun. de Bretagne, Brest, France
Abstract :
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).
Keywords :
data analysis; geophysical signal processing; geophysical techniques; independent component analysis; principal component analysis; spectral analysis; hyperspectral data analysis; independent component analysis; noise adjusted nonorthogonal linear projections; noise-adjusted independant component analysis; noise-adjusted projection pursuit; spatial correlation information; Data analysis; Hyperspectral imaging; Hyperspectral sensors; Independent component analysis; Noise reduction; Noise robustness; Performance analysis; Principal component analysis; Random variables; Vectors;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International
Print_ISBN :
0-7803-7929-2
DOI :
10.1109/IGARSS.2003.1295261