Machine learning techniques for the inversion of planetary hyperspectral images

WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

Volumn , Issue , 2009, Pages

  Bernard Michel, C ^a   Doute S ^b   Fauvel, M ^a   Gardes, L ^a   Girard, S ^a  

^a INRIA   (France)

^b LABORATOIRE D ASTROPHYSIQUE   (France)

Author keywords

Gaussian regularized sliced inversion regression; Hyperspectral images; Mars surface; SVM

Indexed keywords

DIMENSIONAL VECTORS; GAUSSIANS; HIGH DIMENSIONAL SPACES; HYPER-SPECTRAL IMAGES; IMAGE CUBE; INVERSE REGRESSION; MACHINE LEARNING TECHNIQUES; NON-LINEAR REGRESSION; PHYSICAL ANALYSIS; REAL DATA SETS; SIMULATED DATASETS; STRUCTURAL RISK MINIMIZATION; SUPPORT VECTORS MACHINE;

INDEPENDENT COMPONENT ANALYSIS; LEARNING ALGORITHMS; REMOTE SENSING; SIGNAL PROCESSING; SPACE OPTICS; SUPPORT VECTOR MACHINES;

REGRESSION ANALYSIS;

EID: 72049089058     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WHISPERS.2009.5289010     Document Type: Conference Paper

References (13)

1. 2 frost on Io by the modeling of NIMS hyperspectral images," Icarus, vol. 149, pp. 107-132, 2001.
2. R. W. Carlson, P. R. Weissman, W. D. Smythe, J. C. Mahoney, the NIMS Science, and Engineering Teams, "Near infrared spectrometer experiment on Galileo," Space Science Reviews, vol. 60, pp. 457-502, 1992.
3. D.S. Kimes, Y. Knyazikhin, J.L. Privette, A.A. Abuegasim, and F. Gao, "Inversion methods for physically-based models," Remote Sensing Reviews, vol. 18, pp. 381-439, 2000.
4. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2003.
5. C. Bernard-Michel, L. Gardes, and S. Girard, "Gaussian regularized sliced inverse regression," Statistics and Computing, vol. 19, pp. 85-98, 2009.
6. C. Bernard-Michel, S. Douté, M. Fauvel, L. Gardes, and S. Girard, "Retrieval of mars surface physical properties from omega hyperspectral images using regularized sliced inverse regression," Journal of Geophysical Research, To appear, 2009.
7. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "Evaluation of kernels for multiclass classification of hyperspectral remote sensing data," in IEEE ICASSP'06, May 2006.
8. N. Keshava, "Distance metrics and band selection in hyper-spectral processing with application to material identification and spectral librairies," IEEE Trans. Geosci. Remote Sens., vol. 42, pp. 1552-1565, July 2004.
9. K.C. Li, "Sliced inverse regression for dimension reduction," Journal of the American Statistical Association, vol. 86, pp. 316-327, 1991.
10. A. Tarantola, Inverse problem theory and model parameter estimation, SIAM, 2005.
11. R.C. Aster, B. Borchers, and C.H. Thurber, Parameter Estimation and Inverse Problems, Elsevier Academic Press, 2005.
12. C. Bernard-Michel, S. Douté, L. Gardes, and S. Girard, "Inverting hyperspectral images with gaussian regularized sliced inverse regression," in European Symposium on Artificial Neural Networks, Advances in Computational Intelligence and Learning, 2008.
13. C. Bernard-Michel, S. Douté, M. Fauvel, L. Gardes, and S. Girard, "Support vectors machines regression for the estimation of Mars surface physical properties," in European Symposium on Artificial Neural Networks, Advances in Computational Intelligence and Learning, 2009.