| Name | Support | Language | Availability | Edition date | Price | ||
|---|---|---|---|---|---|---|---|
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E-book |
English |
Available |
12/2/2016 |
€39.00 |
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Details
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.
Additional Info
| Author | Jignesh S. Bhatt, Manjunath V. Joshi |
|---|---|
| Published by | SPIE |
| Document type | Book |
| Number of pages | 44 |
| Keyword | PM285 |
