Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A fast fixed-point algorithm for independent component analysis
Neural Computation
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
On the Best Rank-1 and Rank-(R1,R2,. . .,RN) Approximation of Higher-Order Tensors
SIAM Journal on Matrix Analysis and Applications
Survey on tensor signal algebraic filtering
Signal Processing
Multiway filtering applied on hyperspectral images
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
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Hyperspectral images (HSI) are multidimensional and multicomponent data with a huge number of spectral bands providing spectral redundancy. To improve the efficiency of the classifiers the principal component analysis (PCA), referred to as PCAdr, the maximum noise fraction (MNF) and more recently the independent component analysis (ICA), referred to as ICAdr are the most commonly used techniques for dimensionality reduction (DR). But, in HSI and in general when dealing with multi-way data, these techniques are applied on the vectorized images, providing a two-way data. The spatial representation is lost and the spectral components are selected using only spectral information. As an alternative, in this paper, we propose to consider HSI as array data or tensor -instead of matrix- which offers multiple ways to decompose data orthogonally.We develop two news DR methods based on multilinear algebra tools which perform the DR using the PCAdr for the first one and using the ICAdr for the second one. We show that the result of spectral angle mapper (SAM) classification is improved by taking advantage of jointly spatial and spectral information and by performing simultaneously a dimensionality reduction on the spectral way and a projection onto a lower dimensional subspace of the two spatial ways.