Semisupervised PSO-SVM regression for biophysical parameter estimation

IEEE Transactions on Geoscience and Remote Sensing

Volumn 45, Issue 6, 2007, Pages 1887-1895

  Bazi, Yakoub ^a,b   Melgani, Farid ^a,c  

^a IEEE   (Saudi Arabia)

^b JOUF UNIVERSITY   (Saudi Arabia)

^c UNIVERSITY OF TRENTO   (Italy)

Author keywords

Biophysical parameter estimation; Data inflation, multiobjective (MO) optimization; Particle swarm optimizer (PSO); Semisupervised regression; Support vector machine (SVM)

Indexed keywords

DATA INFLATION; PARTICLE SWARM OPTIMIZATION (PSO); REGRESSION SYSTEMS; SEMISUPERVISED REGRESSION;

BIOPHYSICS; INTEGRATION; MULTIOBJECTIVE OPTIMIZATION; PARAMETER ESTIMATION; REGRESSION ANALYSIS; REMOTE SENSING;

SUPPORT VECTOR MACHINES;

EID: 34249816831     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2007.895845     Document Type: Article

References (22)

1. D. G. Goodenough, A. S. Bhogall, H. Chen, and A. Dyk, "Comparison of methods for estimation of Kyoto protocol products of forests from multitemporal Landsat," in Proc. IGARSS, 2001, vol. 2. pp. 764-767.
2. F. Del Frate, A. Ortenzi, S. Casadio, and C. Zehner, "Application of neural algorithms for a real-time estimation of ozone profiles from GOME measurements," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 10, pp. 2263-2270, Oct. 2002.
3. L. Bruzzone and F. Melgani, "Robust multiple estimator systems for the analysis of biophysical parameters from remotely sensed data," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 1, pp. 159-174, Jan. 2005.
4. P. Cipollini, G. Corsini, M. Diani, and R. Grasso, "Retrieval of sea water optically active parameters from hyperspectral data by means of generalized radial basis function neural networks," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 7, pp. 1508-1524, Jul. 2001.
5. H. Zhan, P. Shi, and C. Chen, "Retrieval of oceanic chlorophyll concentration using support vector machines," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 12, pp. 2947-2951, Dec. 2003.
6. Q. Jackson and D. A. Landgrebe, "An adaptive classifier design for high dimensional data analysis with a limited training data set," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 12, pp. 2664-2679, Dec. 2001.
7. A. Demiriz and K. P. Bennett, "Semi-supervised support vector machines," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 1998, pp. 368-374.
8. A. Palau, F. Melgani, and S. B. Serpico, "Cell algorithms with data inflation for non-parametric classification," Pattern Recognit. Lett., vol. 27, no. 7, pp. 781-790, May 2006.
9. Z.-H. Zhou and M. Li, "Semi-supervised regression with co-training," in Proc. 19th Int. Joint Conf. Artif. Intell., 2005, pp. 908-913.
10. J. Kennedy and R. C. Eberhart, Swarm Intelligence. San Mateo, CA: Morgan Kaufmann, 2001.
11. Y. Bazi and F. Melgani, "Toward an optimal SVM classification system for hyperspectral remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3374-3385, Nov. 2006.
12. Z. L. Gaing, "A particle swarm optimization approach for optimum design of PID controller in AVR system," IEEE Trans. Energy Convers., vol. 19, no. 2, pp. 384-391, Jun. 2004.
13. M. Donelli, R. Azzaro, F. G. B. De Natale, and A. Massa, "An innovative computational approach based on a particle swarm strategy for adaptive phased-arrays control," IEEE Trans. Antennas Propag., vol. 54, no. 3, pp. 888-898, Mar. 2006.
14. W. H. Slade, H. W. Ressom, M. T. Musavi, and R. L. Miller, "Inversion of ocean color observations using particle swarm optimization," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 9, pp. 1915-1923, Sep. 2004.
15. S. L. Ho, S. Yang, G. Ni, E. W. C. Lo, and H. C. Wong, "A particle swarm optimization-based method for multiobjective design optimizations," IEEE Trans. Magn., vol. 41, no. 5, pp. 1756-1759, 2002.
16. C. A. Coello, G. T. Pulido, and M. S. Lechuga, "Handling multiobjectives with particle swarm optimization," IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 256-279, Jun. 2004.
17. V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
18. A. Smola and B. Schölkopf, "A tutorial on support vector regression," Roy. Holloway College, Univ. London, London, U.K., NeuroCOLT Tech. Rep. NC-TR-98-030, 1998.
19. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms. Chichester, U.K.: Wiley, 2001.
20. J. E. O'Reilly and S. Maritorena, "SeaBAM evaluation data set," The SeaWiFS Bio-Optical Algorithm Mini-Workshop (SeaBAM), 1997, Santa Barbara, CA: Univ. California. [Online], Available: http://seabass.gsfc.nasa. gov/seabam/seabam.html
21. G. Camps-Valls, L. Bruzzone, J. L. Rojo-Alvarez, and F. Melgani, "Robust support vector regression for biophysical variable estimation from remotely sensed images," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 3, pp. 339-343, Jul. 2006.
22. C.-C. Chang and C.-J. Lin, UBSVM - A Library for Support Vector Machines. [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm