Extreme learning machines for soybean classification in remote sensing hyperspectral images

Neurocomputing

Volumn 128, Issue , 2014, Pages 207-216

  Moreno, Ramón ^a   Corona, Francesco ^b   Lendasse, Amaury ^b   Graña, Manuel ^a   Galvão, Lênio S ^c  

^a UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

^b AALTO UNIVERSITY   (Finland)

^c INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS   (Brazil)

Author keywords

Agricultural remote sensing; Extreme learning machine; Hyperspectral images

Indexed keywords

AGRICULTURAL REMOTE SENSING; CLASSIFICATION PERFORMANCE; CLASSIFICATION PROCESS; EXTREME LEARNING MACHINE; FEED-FORWARD NETWORK; FUNCTIONAL DATA ANALYSIS; HYPER-SPECTRAL IMAGES; STATE-OF-THE-ART ALGORITHMS;

ALGORITHMS; CROPS; KNOWLEDGE ACQUISITION; MAPS; NETWORK LAYERS; PIXELS; REMOTE SENSING; SPECTROSCOPY;

CLASSIFICATION (OF INFORMATION);

ACCURACY; AGRICULTURAL LAND; ALGORITHM; ARTICLE; BRAZIL; CLASSIFICATION; CROP; EXTREME LEARNING MACHINE; IMAGE ANALYSIS; LINEAR SYSTEM; MACHINE LEARNING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NONLINEAR SYSTEM; PRIORITY JOURNAL; REMOTE SENSING; SIGNAL NOISE RATIO; SOYBEAN;

EID: 84893640041     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.03.057     Document Type: Article

References (43)

1. M. Shimoni, G. Tolt, C. Perneel, J. Ahlberg, Detection of vehicles in shadow areas, in: 3rd Workshop on Hyperspectral Image and Signal Processing (WHISPERS), 2011, pp. 1-4.
2. B. Rivard, J. Feng, V. Bushan, M. Lipsett, Infrared reflectance hyperspectral features of athabasca oil sand ore and froth, in: 3rd Workshop on Hyperspectral Image and Signal Processing (WHISPERS), 2011, pp. 1-4.
3. I. Meganem, Y. Deville, S. Hosseini, H. Carfantan, M. Karoui, Extraction of stellar spectra from dense fields in hyperspectral muse data cubes using non-negative matrix factorization, in: 3rd Workshop on Hyperspectral Image and Signal Processing(WHISPERS), 2011, pp. 1-4.
4. D. Krezhova, E. Kirova, Hyperspectral remote sensing of the impact of environmental stresses on nitrogen fixing soybean plants (Glycine max l.), in: 5th International Conference on Recent Advances in Space Technologies (RAST), 2011, pp. 172-177.
5. X. Ye, K. Sakai, Application of airborne hyperspectral imagery to estimating fruit yield in citrus, in: 3rd Workshop on Hyperspectral Image and Signal Processing (WHISPERS), 2011, pp. 1-6.
6. L. Guozhu, S. Kaishan, N. Shuwen, Soybean LAI estimation with in-situ collected hyperspectral data based on BP-neural networks, in: 3rd International Conference on Recent Advances in Space Technologies (RAST '07), 2007, pp. 331-336.
7. Monteiro S.T., Minekawa Y., Kosugi Y., Akazawa T., Oda K. Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery. ISPRS J. Photogr. Remote Sens. 2007, 62(1):2-12.
8. Z. Zhou, Y. Zang, B. Shen, X. Zhou, X. Luo, Detection of cowpea weevil (Callosobruchus maculatus (F.)) in soybean with hyperspectral spectrometry and a backpropagation neural network, in: Sixth International Conference on Natural Computation (ICNC), vol. 3, 2010, pp. 1223-1227.
9. J. Clevers, L. Kooistra, Using hyperspectral remote sensing data for retrieving total canopy chlorophyll and nitrogen content, in: 3rd Workshop on Hyperspectral Image and Signal Processing (WHISPERS), 2011, pp. 1-4.
10. J. Feret, G. Asner, S. Jacquemoud, Regularization of discriminant analysis for the study of biodiversity in humid tropical forests, in: 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011, pp. 1-4.
11. Breunig F.M., Galvao L.S., Formaggio A.R., Epiphanio J.C.N. Classification of soybean varieties using different techniques. case study with hyperion and sensor spectral resolution simulations. J. Appl. Remote Sens. 2011, 5(1):053533.
12. Ramsay J.O., Silverman B.W. Functional Data Analysis 2006, Springer-Verlag, New York. second edition.
13. Ferraty F., Vieu P. Nonparametric Functional Data Analysis 2006, Springer-Verlag, New York.
14. Huang G.-B., Zhu Q.-Y., Siew C.-K. Extreme learning machine. theory and applications. Neurocomputing 2006, 70(1-3):489-501.
15. Miche Y., Sorjamaa A., Bas P., Simula O., Jutten C., Lendasse A. OP-ELM. optimally pruned extreme learning machine. IEEE Trans. Neural Netw. 2010, 21(January (1)):158-162.
16. Rong H.-J., Ong Y.-S., Tan A.-H., Zhu Z. A fast pruned-extreme learning machine for classification problem. Neurocomputing 2008, 72(1-3):359-366.
17. Chen L., Huang G.-B., Pung H.K. Systemical convergence rate analysis of convex incremental feedforward neural networks. Neurocomputing 2009, 72(10-12):2627-2635.
18. Grenander U. Abstract Inference 1981, Wiley, New York.
19. Besse P., Ramsay J.O. Principal component analysis of sampled curves. Psychometrika 1986, 51:285-311.
20. Dauxois J., Nkiet G.M. Measure of association for Hilbertian subspaces and some applications. J. Multivariate Anal. 2002, 82:136-154.
21. He G.Z., Müller H.-G., Wang J.I. Functional canonical analysis for square integrable stochastic processes. J. Multivariate Anal. 2003, 85:54-77.
22. James G.M., Hastie T.J. Functional linear discriminant analysis for irregularly sampled curves. J. R. Stat. Soc. Ser. B 2001, 63:533-550.
23. Abraham C., Cornillon P.A., Matzner-Lober E., Molinari N. Unsupervised curve-clustering using B-Splines. Scand. J. Stat. 2003, 30:581-595.
24. Ramsay J.O., Silverman B.W. Applied Functional Data Analysis. Methods and Case Studies 2002, Springer-Verlag, New York.
25. Rossi F., Conan-Guez B. Functional multi-layer perceptron. a nonlinear tool for functional data analysis. Neural Netw. 2005, 18:45-60.
26. Rossi F., Delannay N., Conan-Guez B., Verleysen M. Representation of functional data in neural networks. Neurocomputing 2005, 64:183-210.
27. Rossi F., Villa-Vialaneix N. Support vector machine for functional data classification. Neurocomputing 2006, 69:730-742.
28. Saeys W., Ketelaere B.D., Darius P. Potential applications of functional data analysis in chemometrics. J. Chemom. 2008, 22:335-344.
29. Ramsay J.O., Hooker G., Campbell D., Cao J. Parameter estimation for differential equations. a generalized smoothing approach. J. R. Stat. Soc. Ser. B 2007, 69:741-796.
30. Cardot H., Faivre R., Goulard M. Functional approaches for predicting land use with the temporal evolution of coarse resolution remote sensing data. J. Appl. Stat. 2003, 30:1185-1199.
31. Harezlak J., Wu M.C., Wang M., Schwartzman A., Cristianini D.C., Lin X. Biomarker discovery for arsenic exposure using functional data. Analysis and feature learning of mass spectrometry proteomic data. J. Proteome Res. 2008, 7:217-224.
32. Morris J.S., Brown P.J., Herrick R.C., Baggerly K.A., Coombes K.R. Bayesian analysis of mass spectrometry proteomics data using Wavelet-based functional mixed models. Biometrics 2008, 64:479-489.
33. Ritz C., Streibig J.C. Functional regression analysis of fluorescence curves. Biometrics 2009, 65:609-617.
34. Goodenough D., Dyk A., Niemann K., Pearlman J., Chen H., Han T., Murdoch M., West C. Processing hyperion and ALI for forest classification. IEEE Trans. Geosci. Remote Sens. 2003, 41(June (6)):1321-1331.
35. Wang T., Yan G., Ren H., Mu X. Improved methods for spectral calibration of on-orbit imaging spectrometers. IEEE Trans. Geosci. Remote Sens. 2010, 48(November (11)):3924-3931.
36. L.B. Liao, P.J. Jarecke, D.A. Gleichauf, T.R. Hedman, Performance characterization of the hyperion imaging spectrometer instrument, in: Proceedings of SPIE on Earth Observing Systems V (SPIE 2000), vol. 4135, 2000, pp. 264-275.
37. Huang G.-B., Wang D. Advances in extreme learning machines (ELM2010). Neurocomputing 2011, 74(16):2411-2412.
38. Xi-Zhao W., Qing-Yan S., Qing M., Jun-Hai Z. Architecture selection for networks trained with extreme learning machine using localized generalization error model. Neurocomputing 2013, 102(1):3-9.
39. Huang G.-B., Chen L. Convex incremental extreme learning machine. Neurocomputing 2007, 70(16-18):3056-3062.
40. Huang G.-B., Chen L. Enhanced random search based incremental extreme learning machine. Neurocomputing 2008, 71(16-18):3460-3468.
41. R. Moreno, M. Graña, About gradient operators on hyperspectral images, in: Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, SciTePress, 2012, pp. 433-437.
42. Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I.H. The weak data mining software. an update. SIGKDD Explor. 2009, 11(1):10-18.
43. Quinlan J.R. C4.5. Programs for Machine Learning 1993, Morgan Kaufmann Publishers.