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Fully constrained linear spectral unmixing algorithm  for hyperspectral image analys
A.Yu. Denisova, V.V. Myasnikov

 

Samara State Aerospace University,

Image Processing Systems Institute, Russian Academy of Sciences

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Full text of article: Russian language.

DOI: 10.18287/0134-2452-2014-38-4-782-789

Pages: 782-789.

Abstract:
In this article, a novel linear spectral unmixing algorithm is proposed and analyzed. The linear spectral mixture defines a model of pixels for hyperspectral images by means of spectral signatures. A set of spectral signatures is assumed to be known. Constraints are imposed on the spectral mixture coefficients: the sum of the coefficients is equal to unity and each coefficient is nonnegative. The results of the algorithm quality and speed analysis are described in the paper.

Key words:
hyperspectral images, linear spectral mixing, constraints, hyperspectral analysis, least squares method.

Citation:
Denisova AY, Myasnikov VV. Fully constrained linear spectral unmixing algorithm for hyperspectral image analys. Computer Optics 2014; 38(4): 782-789. DOI: 10.18287/0134-2452-2014-38-4-782-789.

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