Name
Abstract | ||
|---|---|---|
Hyperspectral remote sensing is a promising method for the farm product monitoring. However, the estimation accuracy is restricted by the multidimensionality and shortage of statistically sufficient number of data. In this paper, a new method is proposed to acquire inherent vegetation-related coordinates on hyperspectral manifold by the combination of unsupervised manifold learning and supervised vegetation-related coordinates estimation. Experimental results show high estimation performance in vegetation-related quantities by the proposed method, i.e. nonlinear structure extraction and improved generalization performance, in comparison with multivariate linear regression based on hyperspectral data. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IGARSS.2012.6350936 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
agriculture,crops,geophysical image processing,learning (artificial intelligence),vegetation mapping,estimation accuracy,farm product monitoring,hyperspectral manifold learning,hyperspectral remote sensing,multidimensionality,nonlinear structure extraction,rice growth state estimation,supervised vegetation related coordinate estimation,unsupervised manifold learning,vegetation related quantities,Hyperspectral image,manifold learning,rice,vegetation index | Nonlinear structure,Computer vision,Vegetation Index,Computer science,Remote sensing,Hyperspectral imaging,Bayesian multivariate linear regression,Artificial intelligence,Nonlinear dimensionality reduction,Economic shortage,Manifold,Machine learning | Conference |
ISSN | ISBN | Citations |
2153-6996 E-ISBN : 978-1-4673-1158-8 | 978-1-4673-1158-8 | 1 |
PageRank | References | Authors |
0.35 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuniaki Uto | 1 | 32 | 10.40 |
| Takahiro Harano | 2 | 1 | 0.35 |
| Yukio Kosugi | 3 | 127 | 26.67 |