Name
Title | ||
|---|---|---|
Generalized bilinear model based nonlinear unmixing using semi-nonnegative matrix factorization |
Abstract | ||
|---|---|---|
Nonlinear spectral mixing models have recently been receiving attention in hyperspectral image processing. This work presents a novel optimization method for nonlinear unmixing based on a generalized bilinear model (GBM), which considers second-order scattering effects. Semi-nonnegative matrix factorization is used for optimization to process a whole image in a matrix form. The proposed method is applied to an airborne hyperspectral image with many endmembers and shows good performance both in unmixing quality and computational cost with simple implementation. The effect of endmember extraction on nonlinear unmixing is investigated and the impact of the nonlinearity on abundance maps is demonstrated. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IGARSS.2012.6351282 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
geophysical image processing,matrix decomposition,optimisation,scattering,abundance maps,airborne hyperspectral image processing,generalized bilinear model,nonlinear spectral mixing model,nonlinear unmixing,optimization,scattering effect,seminonnegative matrix factorization,generalized bilinear model,hyperspectral image,nonlinear unmixing,semi-nonnegative matrix factorization | Matrix form,Computer vision,Endmember,Nonlinear system,Computer science,Matrix decomposition,Hyperspectral imaging,Artificial intelligence,Non-negative matrix factorization,Hyperspectral image processing,Bilinear interpolation | Conference |
ISSN | ISBN | Citations |
2153-6996 E-ISBN : 978-1-4673-1158-8 | 978-1-4673-1158-8 | 4 |
PageRank | References | Authors |
0.48 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naoto Yokoya | 1 | 439 | 36.36 |
| Jocelyn Chanussot | 2 | 9 | 2.04 |
| akira iwasaki | 3 | 193 | 16.71 |