Semi-Supervised Support Vector Biophysical Parameter Estimation

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.

[1]  A. Dyk,et al.  Comparison of methods for estimation of Kyoto Protocol products of forests from multitemporal Landsat , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[2]  Bernhard Schölkopf,et al.  Learning with Hypergraphs: Clustering, Classification, and Embedding , 2006, NIPS.

[3]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[4]  Marco Diani,et al.  Retrieval of sea water optically active parameters from hyperspectral data by means of generalized radial basis function neural networks , 2001, IEEE Trans. Geosci. Remote. Sens..

[5]  Lei Ji,et al.  Forecasting vegetation greenness with satellite and climate data , 2004, IEEE Geoscience and Remote Sensing Letters.

[6]  Quanquan Gu,et al.  Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[7]  Xiao‐Hai Yan,et al.  Development and application of a neural network based ocean colour algorithm in coastal waters , 2005 .

[8]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

[9]  Bernhard Schölkopf,et al.  Cluster Kernels for Semi-Supervised Learning , 2002, NIPS.

[10]  Mikhail Belkin,et al.  Beyond the point cloud: from transductive to semi-supervised learning , 2005, ICML.

[11]  Ping Shi,et al.  Retrieval of oceanic chlorophyll concentration using support vector machines , 2003, IEEE Trans. Geosci. Remote. Sens..

[12]  Gustavo Camps-Valls,et al.  Semi-Supervised Graph-Based Hyperspectral Image Classification , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[13]  J. Privette,et al.  Inversion methods for physically‐based models , 2000 .

[14]  F. Vuolo,et al.  Cost effectiveness of vegetation biophysical parameters retrieval from remote sensing data , 2006, SPIE Remote Sensing.

[15]  José Luis Rojo-Álvarez,et al.  Robust support vector regression for biophysical variable estimation from remotely sensed images , 2006, IEEE Geoscience and Remote Sensing Letters.