Nom | Support | Langue | Disponibilité | Date d'édition | Prix | ||
---|---|---|---|---|---|---|---|
E-book |
Anglais |
Disponible |
02/12/2016 |
39,00 € |
|
Détails
Spectral unmixing is a challenging mixed-pixel decomposition problem that can be addressed by regularization This Spotlight presents methods to obtain better estimates of underlying abundances. It discusses least-squares, total-least squares, and Markov random-field-based frameworks to unmix hyperspectral data. Particular attention is paid to spectral-space-based regularization methods. Detailed theoretical analysis is performed to illustrate the advantages of this approach. The performance of the proposed methods is tested using a simulated database as well as by conducting experiments on real AVIRIS data. Other topics include parameter estimation, noise sensitivity, and time-complexity-related issues. Finally, the primary results of parallel computations are provided for real-time applications.
Informations supplémentaires
Auteur | Jignesh S. Bhatt, Manjunath V. Joshi |
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Edité par | SPIE |
Type de document | Livre |
Nombre de pages | 44 |
Mot-clé | PM285 |