Estimation of the hyperspectral tucker ranks

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...

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Bibliographic Details
Published in2009 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 1281 - 1284
Main Authors Huck, A., Guillaume, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2009
Subjects
Additive noise
Hyperspectral
Hyperspectral imaging
Hyperspectral sensors
Image analysis
Layout
Matrix decomposition
Multidimensional systems
Non-negative Tucker Decomposition (NTD)
Pixel
Ranks
Tensile stress
Tensor
Unmixing
Vectors
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Summary: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.
ISBN:9781424423538
1424423538
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2009.4959825