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Multimedia Tools and Applications

, Volume 77, Issue 20, pp 27061–27074 | Cite as

Efficient classification of the hyperspectral images using deep learning

  • Simranjit SinghEmail author
  • Singara Singh Kasana
Article
  • 211 Downloads

Abstract

Classification techniques applicable to the hyperspectral images do not extract deep features from the hyperspectral image efficiently. In this work, a deep learning approach is proposed to extract the deep features, and these features are utilized to propose a novel framework for classification of the hyperspectral image. The framework uses LPP, DCNN and logistic regression. Data of a hyperspectral image is processed by LPP for dimensionality reduction as it contains a large number of dimensions. Afterward, a DCNN is constructed with Autoencoders which is then passed to the logistic regression for classification. Proposed framework is tested on Indian Pines and Salinas data sets. High accuracy is achieved using the proposed framework in comparison of existing machine learning models.

Keywords

Auto Encoders LPP DCNN HSI Neural networks PCA SVM 

References

  1. 1.
    Aldrich J, Fisher RA (1997) The making of maximum likelihood 1912–1922. Stat Sci 12(3):162–176MathSciNetCrossRefGoogle Scholar
  2. 2.
    Andrychowicz M, Denil M, Gomez S, Hoffman M W, Pfau D, Schaul T, de Freitas N (2016) Learning to learn by gradient descent by gradient descent. In: Neural information processing systems, pp 1–17Google Scholar
  3. 3.
    Burger JE, Gowen AA (2011) The interplay of chemometrics and hyperspectral chemical imaging. In: Workshop on hyperspectral image and signal processing, evolution in remote sensing, pp 3–6Google Scholar
  4. 4.
    Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167CrossRefGoogle 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.
    Chang C-I (2004) New hyperspectral discrimination measure for spectral characterization. Opt Eng 43(8):1777CrossRefGoogle Scholar
  7. 7.
    Chen Y, Lin Z, Zhao X, Member S, Wang G, Gu Y (2014) Deep learning-based classification of hyperspectral data. IEEE J Sel Top Appl Earth Obs Remote Sens 7(6):2094–2107CrossRefGoogle Scholar
  8. 8.
    Fagan ME, DeFries RS, Sesnie SE, Arroyo-Mora JP, Soto C, Singh A, Chazdon (2015) Mapping species composition of forests and tree plantations in northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery. Remote Sens 7(5):5660–5696CrossRefGoogle Scholar
  9. 9.
    Greene WWH (2012) Econometric analysis, vol 97. Prentice Hall, Englewood CliffsGoogle Scholar
  10. 10.
    He X, Niyogi P (2004) Locality preserving projections. Adv Neural Inf Process Syst 16:153Google Scholar
  11. 11.
    Hecht-Nielsen R (1989) Theory of the backpropagation neural network. In: Proceedings of the international joint conference on neural networks, vol 1, pp 593–605Google Scholar
  12. 12.
    Ivakhnenko AG, Lapa VG (1965) Cybernetic predicting devices. CCM Information Corporation, New YorkGoogle Scholar
  13. 13.
    Jolliffe IT (1986) Principal component. Springer series in statistics analysis. Springer, BerlinCrossRefGoogle Scholar
  14. 14.
    Kohonen T (1988) An introduction to neural computing. Neural Netw 1(1):3–16CrossRefGoogle Scholar
  15. 15.
    Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1–9Google Scholar
  16. 16.
    Kruger N, Janssen P, Kalkan S, Lappe M, Leonardis A, Piater J, Wiskott L (2013) Deep hierarchies in the primate visual cortex: what can we learn for computer vision?. IEEE Trans Pattern Anal Mach Intell 35(8):1847–1871CrossRefGoogle Scholar
  17. 17.
    Kruse FA, Lefkoff AB, Boardman JW, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz AFH (1993) The Spectral Image Processing System (SIPS) - interactive visualization and analysis of imaging spectrometer data. Remote Sens Environ 44:145–163CrossRefGoogle Scholar
  18. 18.
    Kruse FA, Boardman JW, Huntington JF (2003) Comparison of airborne hyperspectral data and EO-1 hyperion for mineral mapping. IEEE Trans Geosci Remote Sens 41(6 PART I):1388–1400CrossRefGoogle Scholar
  19. 19.
    Kumar M, Seshasai MVR, Vara Prasad KS, Kamala V, Ramana KV, Dwivedi RS, Roy PS (2011) A new hybrid spectral similarity measure for discrimination among Vigna species. Int J Remote Sens 32(14):4041–4053CrossRefGoogle Scholar
  20. 20.
    Le Roux N, Bengio Y (2010) Deep belief networks are compact universal approximators. Neural Comput 22(8):2192–2207MathSciNetCrossRefGoogle Scholar
  21. 21.
    Melgani F, Bruzzone L (2002) Support vector machines for Classification of hyperspectral remote-sensing images. In: IEEE international geoscience and remote sensing symposium, vol 1, pp 506–508Google Scholar
  22. 22.
    Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science (New York, N.Y.) 290(5500):2323–2326CrossRefGoogle Scholar
  23. 23.
    Sutskever I, Hinton GE (2008) Deep, narrow sigmoid belief networks are universal approximators. Neural Comput 20(11):2629–2636CrossRefGoogle Scholar
  24. 24.
    Tenenbaum JB, De Silva V, Langford JC (2000) Sci Reprint 290:2319–2323Google Scholar
  25. 25.
    Tobergte DR, Curtis S (2013) Laplacian Eigenmap. J Chem Inf Model 53 (9):1689–1699Google Scholar
  26. 26.
    Vane G, Green RO (1993) The airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sens Environ 44(3):127–143CrossRefGoogle Scholar
  27. 27.
    Wu S, Zhong S, Liu Y (2017) Deep residual learning for image steganalysis. In: Multimedia tools and applications, pp 1–17CrossRefGoogle Scholar
  28. 28.
    Yan C, Zhang Y, Xu J, Dai F, Li L, Dai Q, Wu F (2014) A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process Lett 21(5):573–576CrossRefGoogle Scholar
  29. 29.
    Yan C, Zhang Y, Xu J, Dai F, Zhang J, Dai Q, Wu F (2014) Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans Circuits Syst Video Technol 24(12):2077–2089CrossRefGoogle Scholar
  30. 30.
    Yan C, Xie H, Liu S, Yin J, Zhang Y, Dai Q (2018) Effective Uyghur language text detection in complex background images for traffic prompt identification. IEEE Trans Intell Transp Syst 19(1):220–229CrossRefGoogle Scholar
  31. 31.
    Yan C, Xie H, Yang D, Yin J, Zhang Y (2018) Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans Intell Transp Syst 19(1):284–295CrossRefGoogle Scholar
  32. 32.
    Yang C, Everitt JH, Bradford JM (2008) Yield estimation from hyperspectral imagery using Spectral Angle Mapper (SAM). Trans ASABE 51(2):729–737CrossRefGoogle Scholar
  33. 33.
    Yue J, Mao S, Li M (2016) A deep learning framework for hyperspectral image classification using spatial pyramid pooling. Remote Sens Lett 7(9):875–884CrossRefGoogle Scholar
  34. 34.
    Zanaty EA (2012) Support Vector Machines (SVMs) versus Multilayer Perception (MLP) in data classification. Egypt Inf J 13(3):177–183CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentThapar Institute of Engineering and TechnologyPatialaIndia

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