Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval

IEEE Geoscience and Remote Sensing Letters

Volumn 13, Issue 7, 2016, Pages 1012-1016

  Verrelst, Jochem ^a   Dethier, Sara ^b   Rivera, Juan Pablo ^a   Munoz Mari, Jordi ^a   Camps Valls, Gustau ^b   Moreno, Jose ^a  

^a UNIVERSITY OF VALENCIA   (Spain)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Active learning (AL); hybrid retrieval methods; kernel methods; machine learning regression algorithms (MLRAs); PROSAIL; Sentinel 3

Indexed keywords

ALGORITHMS; ALUMINUM; BIOPHYSICS; LEARNING SYSTEMS; MATLAB; RADIATIVE TRANSFER; REMOTE SENSING;

ACTIVE LEARNING METHODS; BIO-PHYSICAL VARIABLES; CHLOROPHYLL CONTENTS; OPERATIONAL RETRIEVALS; OPTICAL REMOTE SENSING DATA; RADIATIVE TRANSFER MODEL; REDUCTION TECHNIQUES; REGRESSION ALGORITHMS;

ARTIFICIAL INTELLIGENCE;

EID: 84971457374     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2016.2560799     Document Type: Article

References (29)

1. M. E. Schaepman et al., "Earth system science related imaging spectroscopy-An assessment," Remote Sens. Environ., vol. 113, pp. S123-S137, 2009.
2. J. Verrelst et al., "Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties-A review," ISPRS J. Photogramm. Remote Sens., vol. 108, pp. 273-290, 2015.
3. F. Baret et al., "LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION. Part 1: Principles of the algorithm," Remote Sens. Environ., vol. 110, no. 3, pp. 275-286, 2007.
4. F. Baret et al., "GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production," Remote Sens. Environ., vol. 137, pp. 299-309, 2013.
5. S. Jacquemoud et al., "PROSPECT+SAIL models: A review of use for vegetation characterization," Remote Sens. Environ., vol. 113, pp. S56-S66, 2009.
6. J. Suykens and J. Vandewalle, "Least squares support vector machine classifiers," Neural Process. Lett., vol. 9, no. 3, pp. 293-300, 1999.
7. J. Verrelst et al., "Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and-3," Remote Sens. Environ., vol. 118, pp. 127-139, 2012.
8. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. New York, NY, USA: MIT Press, 2006.
9. J. Verrelst, L. Alonso, G. Camps-Valls, J. Delegido, and J. Moreno, "Retrieval of vegetation biophysical parameters using Gaussian process techniques," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 5, pp. 1832-1843, May 2012.
10. J. Verrelst et al., "Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods-A comparison," ISPRS J. Photogramm. Remote Sens., vol. 108, pp. 260-272, Oct. 2015.
11. J. Arenas-Garcia, K. Petersen, G. Camps-Valls, and L. Hansen, "Kernel multivariate analysis framework for supervised subspace learning: A tutorial on linear and kernel multivariate methods," IEEE Signal Process. Mag., vol. 30, no. 4, pp. 16-29, Jul. 2013.
12. D. J. MacKay, "Information-based objective functions for active data selection," Neural Comput., vol. 4, pp. 590-604, 1992.
13. D. Tuia, M. Volpi, L. Copa, M. Kanevski, and J. Muñoz-Marí, "A survey of active learning algorithms for supervised remote sensing image classification," IEEE J. Sel. Topics Signal Process., vol. 5, no. 3, pp. 606-617, Jun. 2011.
14. J. Muñoz Marí, D. Tuia, and G. Camps-Valls, "Semisupervised classification of remote sensing images with active queries," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 10, pp. 3751-3763, Oct. 2012.
15. M. Crawford, D. Tuia, and H. Yang, "Active learning: Any value for classification of remotely sensed data?" Proc. IEEE, vol. 101, no. 3, pp. 593-608, Mar. 2013.
16. E. Pasolli, F. Melgani, D. Tuia, F. Pacifici, and W. Emery, "SVM active learning approach for image classification using spatial information," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 4, pp. 2217-2223, Apr. 2014.
17. E. Pasolli, F. Melgani, N. Alajlan, and Y. Bazi, "Active learning methods for biophysical parameter estimation," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 10, pp. 4071-4084, Oct. 2012.
18. Y. Bazi and F. Melgani, "Semisupervised PSO-SVM regression for biophysical parameter estimation," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 6, pp. 1887-1895, Jun. 2007.
19. Y. Bazi and F. Melgani, "Kernel ridge regression with active learning for wind speed prediction," Appl. Energy, vol. 103, pp. 328-340, Mar. 2013.
20. Y. Gu, Z. Jin, and S. Chiu, "Active learning combining uncertainty and diversity for multi-class image classification," IET Comput. Vis., vol. 9, no. 3, pp. 400-407, 2015.
21. F. Douak, N. Benoudjit, and F.Melgani, "A two-stage regression approach for spectroscopic quantitative analysis," Chemometrics Intell. Lab. Syst., vol. 109, no. 1, pp. 34-41, 2011.
22. B. Demir, C. Persello, and L. Bruzzone, "Batch-mode active-learning methods for the interactive classification of remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 3, pp. 1014-1031, Mar. 2011.
23. S. Patra and L. Bruzzone, "A cluster-Assumption based batch mode active learning technique," Pattern Recognit. Lett., vol. 33, no. 9, pp. 1042-1048, 2012.
24. J. Verrelst, E. Romijn, and L. Kooistra, "Mapping vegetation density in a heterogeneous river floodplain ecosystem using pointable CHRIS/PROBA data," Remote Sens., vol. 4, no. 9, pp. 2866-2889, 2012.
25. J. Caicedo, J. Verrelst, J. Munoz-Mari, J. Moreno, and G. Camps-Valls, "Toward a semiautomatic machine learning retrieval of biophysical parameters," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 4, pp. 1249-1259, Apr. 2014.
26. G. Camps-Valls, L. Gómez-Chova, J. Muñoz-Marí, M. Lázaro-Gredilla, and J. Verrelst, simpleR: A simple educational Matlab toolbox for statistical regression, v2.1, Jun. 2013. [Online]. Available: http://isp.uv.es/
27. J. Rivera, J. Verrelst, G. Leonenko, and J. Moreno, "Multiple cost functions and regularization options for improved retrieval of leaf chlorophyll content and LAI through inversion of the PROSAIL model," Remote Sens., vol. 5, no. 7, pp. 3280-3304, 2013.
28. B. Schölkopf and A. Smola, Learning With Kernels-Support Vector Machines, Regularization, Optimization and Beyond. Cambridge, MA, USA: MIT Press, 2002.
29. B. Demir and L. Bruzzone, "A multiple criteria active learning method for support vector regression," Pattern Recognit., vol. 47, no. 7, pp. 2558-2567, Jul. 2014.