Hyperspectral image classification based on multiple reduced kernel extreme learning machine

  • Fei LvEmail author
  • Min Han
Original Article


This paper presents an efficient hyperspectral images classification method based on multiple reduced kernel extreme learning machine (MRKELM). The MRKELM model is developed on the basis of the multiple kernel leaning method and the reduced kernel extreme learning machine method. In the presented MRKELM, the kernel function are not fixed anymore, multiple kernels are adaptively trained as a hybrid kernel and the optimal kernel combination weights are jointly optimized. Finally, two simulation examples, classification of benchmark datasets and classification of hyperspectral images including Indian Pines, University of Pavia, and Salinas respectively, are used testify the performance of the proposed MRKELM method.


Classification Hyperspectral image Reduced kernel extreme learning machine Multiple reduced kernel extreme learning machine 



This work was supported by National Natural Science Foundation of China under Grant no. 61374154 and the National Basic Research Program of China under Grant no. 2013CB430403.


  1. 1.
    Bach FR, Lanckriet GR, Jordan MI (2004) Multiple kernel learning, conic duality, and the smo algorithm. In: Proceedings of the twenty-first international conference on Machine learning, ACM, p 6Google Scholar
  2. 2.
    Bazi Y, Alajlan N, Melgani F, AlHichri H, Malek S, Yager RR (2014) Differential evolution extreme learning machine for the classification of hyperspectral images. Geosci Remote Sens Lett IEEE 11(6):1066–1070CrossRefGoogle Scholar
  3. 3.
    Bellocchio F, Ferrari S, Piuri V, Borghese NA (2012) Hierarchical approach for multiscale support vector regression. IEEE Trans Neural Netw Learn Syst 23(9):1448–1460CrossRefGoogle Scholar
  4. 4.
    Bencherif M, Bazi Y, Guessoum A, Alajlan N, Melgani F, AlHichri H (2015) Fusion of extreme learning machine and graph-based optimization methods for active classification of remote sensing images. Geosci Remote Sens Lett IEEE 12(3):527–531CrossRefGoogle Scholar
  5. 5.
    Camps-Valls G, Bruzzone L (2005) Kernel-based methods for hyperspectral image classification. IEEE Trans Geosci Remote Sens 43(6):1351–1362CrossRefGoogle Scholar
  6. 6.
    Cao W, Wang X, Ming Z, Gao J (2018) A review on neural networks with random weights. Neurocomputing 275:278–287CrossRefGoogle Scholar
  7. 7.
    Chen X, Guo N, Ma Y, Chen G (2012) More efficient sparse multi-kernel based least square support vector machine. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 70–78Google Scholar
  8. 8.
    Deng WY, Ong YS, Zheng QH (2016) A fast reduced kernel extreme learning machine. Neural Netw 76:29–38CrossRefGoogle Scholar
  9. 9.
    Duan L, Tsang IW, Xu D (2012) Domain transfer multiple kernel learning. IEEE Trans Patt Anal Mach Intell 34(3):465–479CrossRefGoogle Scholar
  10. 10.
    Gnen M, Alpaydn E (2011) Multiple kernel learning algorithms. J Mach Learn Res 12:2211–2268MathSciNetGoogle Scholar
  11. 11.
    Grigorievskiy A, Miche Y, Ventel AM, Sverin E, Lendasse A (2014) Long-term time series prediction using op-elm. Neural Netw 51:50–56CrossRefzbMATHGoogle Scholar
  12. 12.
    Gu Y, Wang C, You D, Zhang Y, Wang S, Zhang Y (2012) Representative multiple kernel learning for classification in hyperspectral imagery. IEEE Trans Geosci Remote Sens 50(7):2852–2865CrossRefGoogle Scholar
  13. 13.
    Gu Y, Liu T, Jia X, Benediktsson JA, Chanussot J (2016) Nonlinear multiple kernel learning with multiple-structure-element extended morphological profiles for hyperspectral image classification. IEEE Trans Geosci Remote Sens 54(6):3235–3247CrossRefGoogle Scholar
  14. 14.
    Huang G, Zhu Q, Siew C (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501CrossRefGoogle Scholar
  15. 15.
    Huang G, Wang D, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122CrossRefGoogle Scholar
  16. 16.
    Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513–529CrossRefGoogle Scholar
  17. 17.
    Huang Z, Wang X (2018) Sensitivity of data matrix rank in non-iterative training. Neurocomputing 313:386–391CrossRefGoogle Scholar
  18. 18.
    Iosifidis A, Tefas A, Pitas I (2013) Minimum class variance extreme learning machine for human action recognition. IEEE Trans Circ Syst Video Technol 23(11):1968–1979CrossRefGoogle Scholar
  19. 19.
    Jaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667):78CrossRefGoogle Scholar
  20. 20.
    Kourentzes N, Petropoulos F, Trapero JR (2014) Improving forecasting by estimating time series structural components across multiple frequencies. Int J Forecast 30(2):291–302CrossRefGoogle Scholar
  21. 21.
    Li J, Huang X, Gamba P, Bioucas-Dias JM, Zhang L, Benediktsson JA, Plaza A (2015) Multiple feature learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 53(3):1592–1606CrossRefGoogle Scholar
  22. 22.
    Lichman M (2013) UCI machine learning repository.
  23. 23.
    Liu X, Gao C, Li P (2012) A comparative analysis of support vector machines and extreme learning machines. Neural Netw 33:58–66CrossRefzbMATHGoogle Scholar
  24. 24.
    Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, Blasco J (2013) Selection of optimal wavelength features for decay detection in citrus fruit using the roc curve and neural networks. Food Bioproc Technol 6(2):530–541CrossRefGoogle Scholar
  25. 25.
    Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A (2010) Op-elm: optimally pruned extreme learning machine. IEEE Trans Neural Netw 21(1):158–162CrossRefGoogle Scholar
  26. 26.
    Mohammed A, Minhas R, Jonathan WuQ, Sid-Ahmed M (2011) Human face recognition based on multidimensional pca and extreme learning machine. Patt Recognit 44(10–11):2588–2597CrossRefzbMATHGoogle Scholar
  27. 27.
    Nizar A, Dong Z, Wang Y (2008) Power utility nontechnical loss analysis with extreme learning machine method. IEEE Trans Power Syst 23(3):946–955CrossRefGoogle Scholar
  28. 28.
    Orabona F, Jie L, Caputo B (2012) Multi kernel learning with online-batch optimization. J Mach Learn 13:227–253MathSciNetzbMATHGoogle Scholar
  29. 29.
    Plaza J, Plaza A, Perez R, Martinez P (2009) On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images. Patt Recognit 42(11):3032–3045CrossRefzbMATHGoogle Scholar
  30. 30.
    Qiu S, Lane T (2009) A framework for multiple kernel support vector regression and its applications to sirna efficacy prediction. IEEE/ACM Trans Comput Biol Bioinf 6(2):190–199CrossRefGoogle Scholar
  31. 31.
    Rakotomamonjy A, Bach F, Canu S (2007) More efficiency in multiple kernel learning. In: International conference on machine learning, pp 775–782Google Scholar
  32. 32.
    Rakotomamonjy A, Bach F, Canu S, Grandvalet Y (2008) Simplemkl. J Mach Learn Res 9:2491–2521MathSciNetzbMATHGoogle Scholar
  33. 33.
    Rong H, Huang G, Sundararajan N, Saratchandran P (2009) Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans Syst Man Cybern Part B Cybern 39(4):1067–1072CrossRefGoogle Scholar
  34. 34.
    Samat A, Du P, Liu S, Li J, Cheng L (2014) E2lm: ensemble extreme learning machines for hyperspectral image classification. IEEE J Select Topics Appl Earth Observ Remote Sens 7(4):1060–1069CrossRefGoogle Scholar
  35. 35.
    Shi Z, Han M (2009) \(\gamma\)-c plane and robustness in static reservoir for nonlinear regression estimation. Neurocomputing 72(7):1732–1743CrossRefGoogle Scholar
  36. 36.
    Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222MathSciNetCrossRefGoogle Scholar
  37. 37.
    Song Y, Zheng YT, Tang S, Zhou X, Zhang Y, Lin S, Chua TS (2011) Localized multiple kernel learning for realistic human action recognition in videos. IEEE Trans Circ Syst Video Technol 21(9):1193–1202CrossRefGoogle Scholar
  38. 38.
    Subrahmanya N, Shin YC (2010) Sparse multiple kernel learning for signal processing applications. IEEE Trans Patt Anal Mach Intell 32(5):788–798CrossRefGoogle Scholar
  39. 39.
    Wang X, Cao W (2018) Non-iterative approaches in training feed-forward neural networks and their applications. Soft Comput 22(11):3473–3476CrossRefzbMATHGoogle Scholar
  40. 40.
    Wang X, Zhang T, Wang R (2018a) Noniterative deep learning: incorporating restricted Boltzmann machine into multilayer random weight neural networks. IEEE Trans Syst Man Cybern Syst 1–10Google Scholar
  41. 41.
    Wang XZ, Wang R, Xu C (2018b) Discovering the relationship between generalization and uncertainty by incorporating complexity of classification. IEEE Trans Cybern 48(2):703–715MathSciNetCrossRefGoogle Scholar
  42. 42.
    Wang Z, Wang X (2018) A deep stochastic weight assignment network and its application to chess playing. J Parallel Distrib Comput 117:205–211CrossRefGoogle Scholar
  43. 43.
    Widodo A, Budi I (2012) Multi layer kernel learning for time series forecasting. In: 2012 international conference on advanced computer science and information systems (ICACSIS), IEEE, pp 313–318Google Scholar
  44. 44.
    Wilamowski B, Yu H (2010) Neural network learning without backpropagation. IEEE Trans Neural Netw 21(11):1793–1803CrossRefGoogle Scholar
  45. 45.
    Xue J, Liu Q, Li M, Liu X, Ye Y, Wang S, Yin J (2018) Incremental multiple kernel extreme learning machine and its application in robo-advisors. Soft Comput 22(11):3507–3517CrossRefGoogle Scholar
  46. 46.
    Yang S, Jin H, Yang L, Xu W, Jiao L (2014) Compressive sensing-inspired dual-sparse slfnn for hyperspectral imagery classification. Geosci Remote Sens Lett IEEE 11(1):220–224CrossRefGoogle Scholar
  47. 47.
    Ye Y, Squartini S, Piazza F (2012) On-line extreme learning machine for training time-varying neural networks. Bio-Inspired Comput Appl 6840:49–54CrossRefGoogle Scholar
  48. 48.
    Yu S, Falck T, Daemen A, Tranchevent LC, Suykens JA, De Moor B, Moreau Y (2010) L2-norm multiple kernel learning and its application to biomedical data fusion. BMC Bioinf 11(1):309CrossRefGoogle Scholar
  49. 49.
    Yu S, Tranchevent LC, De Moor B, Moreau Y (2011) L n-norm multiple kernel learning and least squares support vector machines. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 39–88Google Scholar
  50. 50.
    Zhang L, He Z, Liu Y (2017a) Deep object recognition across domains based on adaptive extreme learning machine. Neurocomputing 239:194–203CrossRefGoogle Scholar
  51. 51.
    Zhang L, Liu Y, Deng P (2017b) Odor recognition in multiple e-nose systems with cross-domain discriminative subspace learning. IEEE Trans Instrum Meas 66(7):1679–1692CrossRefGoogle Scholar
  52. 52.
    Zhang L, Wang X, Huang GB, Liu T, Tan X (2018a) Taste recognition in e-tongue using local discriminant preservation projection. IEEE Trans Cybern 1–14Google Scholar
  53. 53.
    Zhang Y, Wang Y, Zhou G, Jin J, Wang B, Wang X, Cichocki A (2018b) Multi-kernel extreme learning machine for eeg classification in brain-computer interfaces. Expert Syst Appl 96:302–310CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina

Personalised recommendations