Modeling the error statistics in support vector regression of surface temperature from infrared data

IEEE Geoscience and Remote Sensing Letters

Volumn 6, Issue 3, 2009, Pages 448-452

  Moser, Gabriele ^a   Serpico, Sebastiano B ^a  

^a UNIVERSITY OF GENOA   (Italy)

Author keywords

Error estimation; Land surface temperature (LST); Sea surface temperature (SST); Supervised regression; Support vector machines (SVMs)

Indexed keywords

BAYESIAN; CRITICAL INPUTS; EFFECTIVE TOOL; ERROR ESTIMATION; GLOBAL SCALE; INFRARED DATA; INFRARED REMOTE SENSING; INPUT PARAMETER; LAND SURFACE TEMPERATURE (LST); METEOROLOGICAL DATA; METEOROLOGICAL MODELS; METEOSAT SECOND GENERATIONS; NONSTATIONARY; NOVEL METHODS; RANDOM VARIABILITY; REGRESSION ERRORS; SEA SURFACE TEMPERATURE (SST); SEA SURFACE TEMPERATURES; SPINNING ENHANCED VISIBLE AND INFRARED IMAGER; SUPERVISED REGRESSION; SUPPORT VECTOR REGRESSIONS; SURFACE TEMPERATURES;

ATMOSPHERICS; ERROR ANALYSIS; ERROR STATISTICS; ESTIMATION; IMAGE RETRIEVAL; MAXIMUM LIKELIHOOD ESTIMATION; RADIOMETERS; RADIOMETRY; REGRESSION ANALYSIS; REMOTE SENSING; SPACE TIME ADAPTIVE PROCESSING; SUBMARINE GEOPHYSICS; SUPPORT VECTOR MACHINES; SURFACE MEASUREMENT; SURFACE PROPERTIES; VECTORS;

ATMOSPHERIC TEMPERATURE;

EID: 67651154157     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2009.2015777     Document Type: Article

References (16)

1. P. Dash, F.-M. Gottsche, F.-S. Olesen, and H. Fischer, "Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends," Int. J. Remote Sens., vol. 23, no. 13, pp. 2563- 2594, 2002.
2. C. J. Merchant and P. L. Borgne, "Retrieval of sea surface temperature from space, based on modeling of infrared radiative transfer: Capabilities and limitations," J. Atmos. Ocean. Technol., vol. 21, no. 11, pp. 1734- 1746, Nov. 2004.
3. M. Zortea, M. De Martino, G. Moser, and S. B. Serpico, "Land surface temperature estimation from infrared satellite data using support vector machines," in Proc. IGARSS, Denver, CO, Jul. 31-Aug. 4, 2006, pp. 2109-2112.
4. V. N. Vapnik, Statistical Learning Theory. New York: Wiley-Interscience, 1998.
5. M. Zortea, "Advanced pattern recognition techniques for environmental information extraction from remotely sensed data," Ph.D. dissertation, Univ. Genoa, Genoa, Italy, 2007.
6. G. Moser and S. B. Serpico, "Automatic parameter optimization for support vector regression for land and sea surface temperature estimation from remote-sensing data," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 3, pp. 909-921, 2009.
7. F. Caparrini and F. Castelli, "Mapping of land-atmosphere heat fluxes and surface parameters with remote sensing data," Boundary-Layer Meteorol., vol. 107, no. 3, pp. 605-633, Jun. 2003.
8. Y. H. Zhou, D. McLaughlin, and D. Entekhabi, "Assessing the performance of the ensemble Kalman filter for land surface data assimilation," Mon. Weather Rev., vol. 134, no. 8, pp. 2128-2142, 2006.
9. C.-J. Lin and R. C. Weng, "Simple probabilistic predictions for support vector regression," National Taiwan Univ., Taipei City, Taiwan, Tech. Rep., 2004. [Online]. Available: http://www.csie.ntu.edu.tw/∼cjlin/ papers/svrprob.pdf
10. J. B. Gao, S. R. Gunn, and C. J. Harris, "A probabilistic framework for SVM regression and error bar estimation," Mach. Learn., vol. 46, no. 1-3, pp. 71-89, Jan. 2002.
11. M. H. Law and J. T. Kwok, "Bayesian support vector regression," in Proc. AISTATS, Key West, FL, 2001, pp. 239-244.
12. W. Chu, S. Keerthi, and C. J. Ong, "Bayesian support vector regression with a unified loss function," IEEE Trans. Neural Netw., vol. 15, no. 1, pp. 29-44, Jan. 2004.
13. J. B. Gao, S. R. Gunn, and C. J. Harris, "Mean field method for the support vector machine regression," Neurocomputing, vol. 50, pp. 391-405, Jan. 2003.
14. J. B. Gao, S. R. Gunn, and C. J. Harris, "SVM regression through variational methods and its sequential implementation," Neurocomputing, vol. 55, no. 1/2, pp. 151-167, 2003.
15. W. H. Press, S. A. Teukolsky, W. T. Wetterling, and B. P. Flannery, Numerical Recipes in C. Cambridge, U.K.: Cambridge Univ. Press, 2002.
16. G. Moser and S. B. Serpico, "Land and sea surface temperature estimation by support vector regression," in Kernel Methods for Remote Sensing Data Analysis, G. Camps-Valls and L. Bruzzone, Eds. New York: Wiley, 2009