Nonnegative Block-Term Decomposition with the β-Divergence: Joint Data Fusion and Blind Spectral Unmixing

We present a new method for solving simultaneously hyperspectral super-resolution and spectral unmixing of the unknown super-resolution image. Our method relies on three key elements: (1) the nonnegative decomposition in rank-(L r , L r , 1) block-terms, (2) joint tensor factorization with multiplic...

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Bibliographic Details
Main Authors: Prevost, C., Leplat, V.
Format: Conference Proceeding
Language:English
Subjects:
blind spectral unmixing
blockterm decomposition
Computational efficiency
Data integration
hyperspectral super-resolution
Linear programming
Nonnegative tensor factorization
Signal processing
Signal processing algorithms
Superresolution
Tensors
β-divergence
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Summary:We present a new method for solving simultaneously hyperspectral super-resolution and spectral unmixing of the unknown super-resolution image. Our method relies on three key elements: (1) the nonnegative decomposition in rank-(L r , L r , 1) block-terms, (2) joint tensor factorization with multiplicative updates, and (3) the formulation of a family of optimization problems with β-divergences objective functions. We come up with a family of simple, robust and efficient algorithms, adaptable to various noise statistics. Experiments show that our approach competes favorably with state-of-the-art methods for solving both problems at hand for various noise statistics.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10096100