Title :
Semi-Supervised Support Vector Biophysical Parameter Estimation
Author :
Camps-Valls, G. ; Munoz-Mari, J. ; Gomez-Chova, Luis ; Calpe-Maravilla, J.
Author_Institution :
Dept. Eng. Electron., Univ. of Valencia, Valencia
Abstract :
Two kernel-based methods for semi-supervised regression are presented. The methods rely on building a graph or hypergraph Laplacian with both the labeled and unlabeled data, which is further used to deform the training kernel matrix. The deformed kernel is then used for support vector regression (SVR). The semi-supervised SVR methods are sucessfully tested in LAI estimation and ocean chlorophyll concentration prediction from remotely sensed images.
Keywords :
geophysical signal processing; geophysical techniques; image processing; oceanography; regression analysis; remote sensing; support vector machines; vegetation; LAI estimation; biophysical parameter estimation; hypergraph Laplacian building; kernel based methods; ocean chlorophyll concentration prediction; remotely sensed images; semisupervised support vector regression; training kernel matrix deformation; Condition monitoring; Kernel; Laplace equations; Neural networks; Noise robustness; Oceans; Parameter estimation; Remote monitoring; Spatial resolution; Testing;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-2807-6
DOI :
10.1109/IGARSS.2008.4779554
