A Gradient Based Method for Fully Constrained Least-Squares Unmixing of Hyperspectral Images
Résumé
Linear unmixing of hyperspectral images is a popular approach to determine and quantify materials in sensed images. The linear unmixing problem is challenging because the abundances of materials to estimate have to satisfy non-negativity and full-additivity constraints. In this paper, we investigate an iterative algorithm that integrates these two requirements into the coefficient update process. The constraints are satisfied at each iteration without using any extra operations such as projections. Moreover, the mean transient behavior of the weights is analyzed analytically, which has never been seen for other algorithms in hyperspectral image unmixing. Simulation results illustrate the effectiveness of the proposed algorithm and the accuracy of the model.
Mots clés
non-negativity
sparsity
hyperspectral
gradient methods
geophysical image processing
least squares approximations
remote sensing
gradient based method
constrained least-squares unmixing
hyperspectral images
Mathematical model
Materials
Hyperspectral imaging
hyperspectral image unmixing
mean transient behavior
coefficient update process
Hyperspectral imagery
linear unmixing
Pixel
Equations
Signal processing algorithms
sensed images
linear unmixing problem
non-negativity constraints
full-additivity constraints
iterative algorithm
estimation under constraints
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|
Loading...