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Blind decomposition of low-dimensional multi-spectral image by sparse component analysis

Kopriva, Ivica; Cichocki, Andrzej (2009) Blind decomposition of low-dimensional multi-spectral image by sparse component analysis. Journal of Chemometrics, 23 (11). pp. 590-597. ISSN 0886-9383

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Abstract

Multilayer hierarchical alternating least square nonnegative matrix factorization approach has been applied to blind decomposition of low-dimensional multi-spectral image. Performance of the algorithm is invariant with respect to statistical (in)dependence between materials present in the image that is an assumption upon which many existing blind source separation methods depend. The proposed method performs blind decomposition exploiting spectral diversity and spatial sparsity between the materials present in the image. Unlike many existing blind source separation methods the method is capable to estimate the unknown number of materials present in the image. This number can be less than, equal to or greater than the number of spectral bands. Performance of the method is evaluated on underdetermined blind source separation problems associated with blind decompositions of experimental red-green-blue images composed of four materials. The proposed algorithm showed best performance in comparison with methods based on -norm minimization: linear programming and interior-point methods. In addition to tumor demarcation problem demonstrated in the paper, other areas that can also benefit from proposed method are cell and chemical imaging.

Item Type: Article
Additional Information: This is the peer reviewed version of the following article: Blind decomposition of low-dimensional multi-spectral image by sparse component analysis, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/cem.1257/abstract. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Uncontrolled Keywords: multi-spectral imaging; chemical imaging; cell imaging; sparse component analysis; nonnegative matrix factorization
Subjects: NATURAL SCIENCES > Mathematics > Applied Mathematics and Mathematical Modeling
TECHNICAL SCIENCES > Computing > Data Processing
Divisions: Division of Laser and Atomic Research and Development
Projects:
Project titleProject leaderProject codeProject type
Multispectral data analysis (Analiza višespektralih podataka)-Ivica Kopriva098-0982903-2558MZOS
Depositing User: Ivica Kopriva
Date Deposited: 04 Dec 2015 08:34
Last Modified: 04 Dec 2015 08:41
URI: http://fulir.irb.hr/id/eprint/2378
DOI: 10.1002/cem.1257

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