Retrieval of oceanic chlorophyll concentration with relevance vector machines

Remote Sensing of Environment

Volumn 105, Issue 1, 2006, Pages 23-33

  Camps Valls, Gustavo ^a   Gómez Chova, Luis ^a   Muñoz Marí, Jordi ^a   Vila Francés, Joan ^a   Amorós López, Julia ^a   Calpe Maravilla, Javier ^a  

^a UNIVERSITY OF VALENCIA   (Spain)

Author keywords

Biophysical parameter; Concentration prediction; Kernel; Multispectral; Oceanic chlorophyll; Relevance vector machine; Sparsity; Support vector machine

Indexed keywords

ALGORITHMS; CONCENTRATION (PROCESS); OCEANOGRAPHY; REFLECTION; REGRESSION ANALYSIS; REMOTE SENSING; VECTORS;

BIOPHYSICAL PARAMETER; KERNEL; MULTISPECTRAL; RELEVANCE VECTOR MACHINE (RVM); SPARSITY; SUPPORT VECTOR MACHINE;

CHLOROPHYLL;

CHLOROPHYLL A; INVERSE PROBLEM; REMOTE SENSING; SEA SURFACE; VECTOR;

EID: 33750061617     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2006.06.004     Document Type: Article

References (42)

1. Balaguer-Ballester E., Camps-Valls G., Carrasco-Rodriguez J.L., Soria-Olivas E., and del Valle-Tascon S. Effective 1-day ahead prediction of hourly surface ozone concentrations in eastern Spain using linear models and neural networks. Ecological Modelling 156 (2002) 27-41
2. Bruzzone L., Casadio S., Cossu R., Sini F., and Zehner C. A system for monitoring NO2 emissions from biomass burning by using GOME and ATSR-2 data. International Journal of Remote Sensing 24 (2003) 1709-1721
3. Bruzzone L., and Melgani F. Robust multiple estimator systems for the analysis of biophysical parameters from remotely sensed data. IEEE Transactions on Geoscience and Remote Sensing 43 1 (2005, Jan) 159-174
4. Camps-Valls G., and Bruzzone L. Regularized methods for hyperspectral image classification. SPIE international symposium remote sensing, (2004, Set), Gran Canaria, Spain
5. Camps-Valls G., and Bruzzone L. Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 43 6 (2005) 1351-1362
6. Camps-Valls G., Bruzzone L., Rojo-Álvarez J.L., and Melgani F. Robust support vector regression for biophysical parameter estimation from remotely sensed images. IEEE Geoscience and Remote Sensing Letters 3 3 (2006) 1-5
7. Camps-Valls G., Gomez-Chova L., Vila-Francés J., Amorós-López J., Muñoz-Marí J., and Calpe-Maravilla J. Relevance vector machines for sparse learning of biophysical parameters. SPIE international symposium remote sensing, Brugge, Belgium (2005, Set)
8. In: Camps-Valls G., Rojo-Álvarez J.L., and Martínez-Ramón M. (Eds). Kernel Methods in Bioengineering, Signal and Image Processing (2006), Idea Group Inc., Hershey, PA, USA
9. Camps-Valls G., Serrano-López A., Gómez-Chova L., Martín J.D., Calpe J., and Moreno J. Regularized RBF networks for hyperspectral data classification. International conference on image recognition, ICIAR 2004. Lecture notes in computer science. (2004, Oct), Springer-Verlag, Porto, Portugal
10. Canu S., and Rakotomamonjy A. Ozone peak and pollution forecasting using support vectors. IFAC workshop on environmental modelling (2001), International Federation of Automatic Control (IFAC): Yokohama
11. Cipollini P., Corsini G., Diani M., and Grass R. Retrieval of sea water optically active parameters from hyperspectral data by means of generalized radial basis function neural networks. IEEE Transactions on Geoscience and Remote Sensing 39 (2001) 1508-1524
12. Courant, R., & Hilbert, D., (1953). Methods of Mathematical Physics. Interscience Publications. John Wiley and Sons, New York, U.S.A.
13. Cristianini N., and Shawe-Taylor J. An introduction to support vector machines (2000), Cambridge University Press, Cambridge, UK. http://www.supportvector.%20net http://www.supportvector. net
14. De Martino M., Mantero P., Serpico S.B., Carta E., Corsini G., and Grasso R. Water quality estimation by neural networks based on remotely sensed data analysis. Proc. of the International Workshop on Geo-spatial Knowledge Processing for Natural Resource Management (2002) 54-58 Varese, Italy
15. Dempster A.P., Laird N.M., and Rubin D.B. Maximum likelihood from incomplete data via EM algorithm. (with discussion). Journal of the Royal Statistical Society B 39 (1977) 1-38
16. Dzwonkowski B., and Yan X.-H. Development and application of a neural network based colour algorithm in coastal waters. International Journal of Remote Sensing 26 6 (2005, March) 1175-1200
17. Fletcher R. Practical methods of optimization. (2nd edition) (1987), John Wiley & Sons Inc
18. Goodenough D.G., Bhogall A.S., Chen H., and Dyk A. Comparison of methods for estimation of Kyoto Protocol products of forests from multitemporal LANDSAT. Proc. of the intern. geosc. and rem. sens. symp. IGARSS '01 Vol. 2 (2001), University of California, Santa Barbara, USA 764-767
19. Gordon H.R., Brown O.B., Evans R.H., Brown J.W., and Smith R.C. A semianalytic radiance model of ocean color. Journal of Geophysical Research 93 (1988) 10909-10924
20. Ji L., and Peters A.J. Forecasting vegetation greenness with satellite and climate data. IEEE Geoscience and Remote Sensing Letters 1 1 (2004, Jan) 2844-2860
21. Keerthi S.S., and Lin C.-J. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation 15 7 (2003) 1667-1689
22. Keiner L.E. Estimating oceanic chlorophyll concentrations with neural networks. International Journal of Remote Sensing 20 1 (1999, Jan) 189-194
23. Kimes D., Knyazikhin Y., Privette J., Abuelgasim A., and Gao F. Inversion methods for physically-based models. Remote Sensing Reviews 18 (2000) 381-439
24. Kwiatkowska E., and Fargion G. Application of machine-learning techniques toward the creation of a consistent and calibrated global chlorophyll concentration baseline dataset using remotely sensed ocean color data. IEEE Transactions on Geoscience and Remote Sensing 41 12 (2003, Dec) 2844-2860
25. Lapin, L.L. (1983). Probability and statistics for modern engineering. Duxbury Press. PWS Publishers.
26. Lillesand T.M., Kiefer R.W., and Chipman J.W. Remote sensing and image interpretation. (5th edition) (2004), John Wiley, New York
27. Lin H.-T., and Lin C.-J. A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Tech. rep (2003), National Taiwan University, Department of Computer Science and Information Engineering. http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf available at http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf
28. Maritorena, S., & O'Reilly, J. (2000). OC2v2: Update on the initial operational SeaWiFS chlorophyll algorithm in "SeaWiFS postlaunch calibration and validation analyses". Vol. 11. John Wiley & Sons, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA, pp. 3-8.
29. McClain C.R., and Fargion G.S. SIMBIOS project 2003 annual report. Tech. Rep (2003), NASA/TM. http://seabass.gsfc.nasa.gov/ 2003, available at http://seabass.gsfc.nasa.gov/
30. Morel A., and Maritorena S. Bio-optical properties of oceanic waters: A reappraisal. Journal of Geophysical Research 106 (2001) 7763-7780
31. O'Hagan A. Bayesian inference, volume 2B of Kendall's advanced theory of statistics (1994), Arnold, London, United Kingdom
32. O'Reilly J.E., and Maritorena S. SeaBAM evaluation data set. In: The SeaWiFS bio-optical algorithm mini-workshop (SeaBAM) (1997), University of California, Santa Barbara, USA. http://seabass.gsfc.nasa.gov/seabam/seabam.html Available at http://seabass.gsfc.nasa.gov/seabam/seabam.html. Last visited Aug 2005
33. O'Reilly J.E., Maritorena S., Mitchell B.G., Siegel D.A., Carder K., Garver S.A., et al. Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research 103 C11 (1998, Oct) 24937-24953
34. Rasmussen C.E., and Quiñonero-Candela J. Healing the relevance vector machine through augmentation. In: De Raedt L., and Wrobel S. (Eds). Proceedings of the ICML 2005 (2005, June) 689-696
35. Schölkopf B., and Smola A. Learning with kernels - Support vector machines, regularization, optimization and beyond (2002), MIT Press Series
36. Smola A.J., and Schölkopf B. A tutorial on support vector regression. Statistics and Computing 14 (2004) 199-222
37. Tipping M. The relevance vector machine. In: Solla S.A., Leen T.K., and Müller K.-R. (Eds). Advances in neural information processing systems Vol. 12 (2000), MIT Press, Cambridge, Mass
38. Tipping M.E. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1 (2001) 211-244
39. Tipping M.E., and Faul A.C. Fast marginal likelihood maximisation for sparse Bayesian models. In: Bishop C.M., and Frey B.J. (Eds). Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics (2003), JanKey West, FL 3-6
40. Vapnik V.N. Statistical learning theory (1998), John Wiley & Sons, New York
41. Zhan H. Application of support vector machines in inverse problems in ocean color remote sensing. Studies in fuzziness and soft computing. (1st edition) (2005), Springer-Verlag, Berlin 387-397 chapter 18
42. Zhan H., Shi P., and Chen C. Retrieval of oceanic chlorophyll concentration using support vector machines. IEEE Transactions on Geoscience and Remote Sensing 41 12 (2003) 2947-2951