Title :
Estimation of the hyperspectral tucker ranks
Author :
Huck, Alexis ; Guillaume, Mireille
Author_Institution :
Inst. Fresnel, Marseille
Abstract :
In hyperspectral image analysis, one often assumes that observed pixel spectra are linear combinations of pure substance spectra. Unmixing a hyperspectral image consists in finding the number of pure substances in the scene, finding their spectral signatures and estimating the abundance fraction of each pure substance spectrum in each spectral pixel. In this paper, we show that the tensor Tucker decomposition could be considered to solve this problem, and a preliminary problem to overcome consists in estimating the 3 required data Tucker ranks, corresponding to the 3 dimensions of the data cube. Then, we propose an optimal method to estimate them.
Keywords :
geophysical signal processing; image processing; matrix decomposition; spectral analysis; tensors; data cube; hyperspectral image analysis; optimal method; pixel spectra; pure substance spectra; spectral signature; tensor Tucker matrix decomposition; Additive noise; Hyperspectral imaging; Hyperspectral sensors; Image analysis; Layout; Matrix decomposition; Multidimensional systems; Pixel; Tensile stress; Vectors; Hyperspectral; Non-negative Tucker Decomposition (NTD); Ranks; Tensor; Unmixing;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2009.4959825