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Communication Dans Un Congrès Année : 2009

Linear unmixing of hyperspectral images using a scaled gradient method

Résumé

This paper addresses the problem of linear unmixing for hyperspectral imagery. This problem can be formulated as a linear regression problem whose regression coefficients (abundances) satisfy sum-to-one and positivity constraints. Two scaled gradient iterative methods are proposed for estimating the abundances of the linear mixing model. The first method is obtained by including a normalization step in the scaled gradient method. The second method inspired by the fully constrained least squares algorithm includes the sum-to-one constraint in the observation model with an appropriate weighting parameter. Simulations on synthetic data illustrate the performance of these algorithms.
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Dates et versions

hal-04248408 , version 1 (18-10-2023)

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Citer

C. Theys, Nicolas Dobigeon, Jean-Yves Tourneret, Henri Lantéri. Linear unmixing of hyperspectral images using a scaled gradient method. 15th IEEE Workshop on Statistical Signal Processing (SSP 2009), IEEE, Aug 2009, Cardiff, Wales, United Kingdom. pp.729--732, ⟨10.1109/SSP.2009.5278458⟩. ⟨hal-04248408⟩
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