Support vector machines for crop classification using hyperspectral data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2652, Issue , 2003, Pages 134-141

  Camps Valls, G ^a   Gomez Chova L ^a   Calpe Maravilla, J ^a   Soria Olivas, E ^a   Martin Guerrero J D ^a   Moreno, J ^a  

^a UNIVERSITY OF VALENCIA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CROPS; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; IMAGE CLASSIFICATION; MULTILAYER NEURAL NETWORKS; PATTERN RECOGNITION; RADIAL BASIS FUNCTION NETWORKS; SPECTROSCOPY;

CROP CLASSIFICATION; HIGH-DIMENSIONAL; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL DATA; MULTI-LAYER PERCEPTRONS; NEURAL FUZZY INFERENCE SYSTEMS; NEURAL MODELS; RADIAL BASIS FUNCTION(RBF);

SUPPORT VECTOR MACHINES;

EID: 32644440024     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-44871-6_16     Document Type: Article

References (11)

1. P. S. Bradley, U. M. Fayyad, and O. L. Mangasarian. Mathematical programming for data mining: formulations and challenges. INFORMS Journal on Computing, 11(3):217-238, 1999.
2. R. Fletcher. Practical Methods of Optimization. John Wiley & Sons, Inc. 2nd Edition, 1987.
3. L. Gómez, J. Calpe, J. D. Martín, E. Soria, E. Camps-Valls, and J. Moreno. Semi-supervised method for crop classification using hyperspectral remote sensing images. In 1st International Symposium. Recent Advantages in Quantitative Remote Sensing., Torrent, Spain., Set 2002.
4. L. Gómez-Chova, J. Calpe, E. Soria, G. Camps-Valls, J. D. Martín, and J. Moreno. CART-based feature selection of hyperspectral images for crop cover classification. In IEEE International Conference on Image Processing, 2003.
5. J. A. Gualtieri and S. Chettri. Support vector machines for classification of hyperspectral data. In International Geoscience and Remote Sensing, 2000.
6. S. Haykin. Neural Networks: A Comprehensive Foundation. Prentice Hall, 1999.
7. Jang Jyh-Shing Roger, Sun Chuen-Tsai, and Mizutani Eiji. Neuro-Fuzzy and Soft-Computing. Prentice Hall, 1997.
8. Y. Lin, Y. Lee, and G. Wahba. Support Vector Machines for classification in non-standard situations. Department of Statistics TR 1016, University of Wisconsin-Madison, 2000.
9. B. Schölkopf and A. Smola. Learning with Kernels - Support Vector Machines, Regularization, Optimization and Beyond. MIT Press Series, 2001. Partially available from http://www.learning-with-kernels.org.
10. V. N. Vapnik. Statistical Learning Theory. John Wiley & Sons, New York, 1998.
11. J. Zhang, Y. Zhang, and T. Zhou. Classification of hyperspectral data using support vector machine. In IEEE International Conference on Image Processing, pages 882-885, 2001.