[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Spatial-aware hyperspectral image classification via multifeature kernel dictionary learning

  • Regular Paper
  • Published:
International Journal of Data Science and Analytics Aims and scope Submit manuscript

Abstract

Sparse representation based on dictionary learning has yielded impressive effects on hyperspectral image (HSI) classification. But most of these methods utilize only the single spectral feature of HSI and advanced features are not considered, such that the discriminability of sparse representation coefficients is relatively weak. In this paper, we propose a novel multifeature spatial aware dictionary learning model by incorporating complementary across-feature and contextual information obtaining from HSI. The newly developed model, by designing a joint sparse regularization term for pixels represented by several complementary yet correlated features in a contextual group, makes the learning sparse coefficients have enough discriminability. Also, in order to further improve the discrimination ability of coding coefficients, utilizing kernel trick, we design the corresponding kernel extension of the newly proposed model. Based on the newly presented models, we give two corresponding discriminant dictionary learning algorithms. The experimental results on Indian Pines and University of Pavia images show that the effectiveness of the proposed algorithms, which also validate that our algorithms can obtain more discriminant coding coefficients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Solomon, J., Rock, B.: Imaging spectrometry for earth remote sensing. Science 228(4704), 1147–1152 (1985)

    Article  Google Scholar 

  2. Chang, C.I.: Hyperspectral Data Exploitation: Theory and Applications. Wiley-Interscience, Hoboken (2007)

    Book  Google Scholar 

  3. Yang, H.: A back-propagation neural network for mineralogical mapping from AVIRIS data. Int. J. Remote Sens. 20(1), 97–110 (1999)

    Article  Google Scholar 

  4. Benediktsson, J.A., Swain, P.H., Ersoy, O.K.: Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data. Int. J. Remote Sens. 14(15), 2883–2903 (1993)

    Article  Google Scholar 

  5. Mernyi, E.: Intelligent understanding of hyperspectral images through self-organizing neural maps. In: Proceedings 2nd International Conference CITSA, pp. 30–35 (2005)

  6. Hernndezespinosa, C., Fernndezredondo, M., Torressospedra, J.: Some experiments with ensembles of neural networks for classification of hyperspectral images. In: Advances in Neural Networks—Isnn 2004, International Symposium on Neural Networks, Dalian, China, August 19–21, 2004, Proceedings. DBLP, pp. 912–917 (2004)

  7. Zhou, H., Mao, Z., Wang. D.: Classification of coastal areas by airborne hyperspectral image. In: Optical Technologies for Atmospheric, Ocean, and Environmental Studies. International Society for Optics and Photonics, pp. 471–476 (2005)

  8. Goel, P.K., Prasher, S.O., Patel, R.M., Landry, J.A., Bonnell, R.B., Viau, A.A.: Classification of hyper-spectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn. Comput. Electron. Agric. 39(2), 67–93 (2003)

    Article  Google Scholar 

  9. Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, San Diego (2013)

    MATH  Google Scholar 

  10. Vaiphasa, C.: Innovative genetic algorithm for hyperspectral image classification. In: Proceedings Of International Conference on Map Asia, p. 45 (2003)

  11. Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42(8), 1778–1790 (2004)

    Article  Google Scholar 

  12. Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2), 121–167 (1998)

    Article  Google Scholar 

  13. Du, P., Tan, K., Xing, X.: A novel binary tree support vector machine for hyperspectral remote sensing image classification. Opt. Commun. 285(13), 3054–3060 (2012)

    Article  Google Scholar 

  14. Li, J., Bioucas-Dias, J.M., Plaza, A.: Hyperspectral image segmenta-tion using a new Bayesian approach with active learning. IEEE Trans. Geosci. Remote Sens. 49(10), 3947–3960 (2011)

    Article  Google Scholar 

  15. Fauvel, M., Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Tilton, J.C.: Advances in spectral–spatial classification of hyperspectral images. Proc. IEEE 101(3), 652–675 (2013)

    Article  Google Scholar 

  16. Li, J., Marpu, P.R., Plaza, A., Bioucas-Dias, J.M., Benediktsson, J.A.: Generalized composite kernel framework for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 51(9), 4816–4829 (2013)

    Article  Google Scholar 

  17. Camps-Valls, G., Gomez-Chova, L., Muoz-Mar, J., Vila-Frances, J., Calpe-Maravilla, J.: Composite kernels for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 3(1), 93–97 (2006)

    Article  Google Scholar 

  18. Hang, R., Liu, Q., Song, H., Sun, Y.: Matrix-based discriminant subspace ensemble for hyperspectral image spatialspectral feature fusion. IEEE Trans. Geosci. Remote Sens. 54(2), 783–794 (2016)

    Article  Google Scholar 

  19. Hang, R., Liu, Q., Sun, Y., Yuan, X., Pei, H., Plaza, J.: Robust matrix discriminative analysis for feature extraction from hyperspectral images. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 10(5), 2002–2011 (2017)

    Article  Google Scholar 

  20. Tarabalka, Y., Benediktsson, J.A., Chanussot, J.: Spectralspatial classification of hyperspectral imagery based on partitional clustering techniques. IEEE Trans. Geosci. Remote Sens. 47(8), 2973–2987 (2009)

    Article  Google Scholar 

  21. Bruckstein, A.M., Donoho, D.L., Elad, M.: From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev. 51(1), 34–81 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  22. Chen, Y., Nasrabadi, N.M., Tran, T.D.: Hyperspectral image classification via kernel sparse representation. IEEE Trans. Geosci. Remote Sens. 51(1), 217–231 (2013)

    Article  Google Scholar 

  23. Qian, Y., Ye, M., Zhou, J.: Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features. IEEE Trans. Geosci. Remote Sens. 51(4), 2276–2291 (2013)

    Article  Google Scholar 

  24. Chen, Y., Nasrabadi, N.M., Tran, T.D.: Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans. Geosci. Remote Sens. 49(10), 3973–3985 (2011)

    Article  Google Scholar 

  25. Tropp, J.A., Gilbert, A.C., Strauss, M.J.: Algorithms for simultaneous sparse approximation. Part I: greedy pursuit. Signal Process. 86(3), 572–588 (2006)

    Article  MATH  Google Scholar 

  26. Wang, Z., Nasrabadi, N.M., Huang, T.S.: Spatialspectral classification of hyperspectral images using discriminative dictionary designed by learning vector quantization. IEEE Trans. Geosci. Remote Sens. 52(8), 4808–4822 (2014)

    Article  Google Scholar 

  27. Huang, K., Aviyente, S.: Sparse representation for signal classification. In: Advances in neural information processing systems, pp. 609–616 (2007)

  28. Yang, M., Dai, D., Shen, L., Gool, L.V.: Latent dictionary learning for sparse representation based classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4138–4145 (2014)

  29. Yang, M., Zhang, L., Feng, X., Zhang, D.: Sparse representation based fisher discrimination dictionary learning for image classification. Int. J. Comput. Vis. 109(3), 209–232 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  30. Wang, Z., Liu, J., Xue, J.H.: Joint sparse model-based discriminative K-SVD for hyperspectral image classification. Signal Process. 133, 144–155 (2017)

    Article  Google Scholar 

  31. Sun, Y., Liu, Q., Tang, J., Tao, D.: Learning discriminative dictionary for group sparse representation. IEEE Trans. Image Process. 23(9), 3816–3828 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  32. Charles, A.S., Olshausen, B.A., Rozell, C.J.: Learning sparse codes for hyperspectral imagery. IEEE J. Sel. Top. Signal Process. 5(5), 963–978 (2011)

    Article  Google Scholar 

  33. Soltani-Farani, A., Rabiee, H.R., Hosseini, S.A.: Spatial-aware dictionary learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 53(1), 527–541 (2015)

    Article  Google Scholar 

  34. Zhang, E., Zhang, X., Jiao, L., Hou, B.: Weighted multifeature hyperspectral image classification via kernel joint sparse representation. Neurocomputing 178, 71–86 (2016)

    Article  Google Scholar 

  35. Shekhar, S., Patel, V.M., Nasrabadi, N.M., Chellappa, R.: Joint sparse representation for robust multimodal biometrics recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 113–126 (2014)

    Article  Google Scholar 

  36. Yuan, X.T., Liu, X., Yan, S.: Visual classification with multitask joint sparse representation. IEEE Trans. Image Process. 21(10), 4349–4360 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  37. Li, J., Zhang, H., Zhang, L., Zhang, L.: Joint collaborative representation with multitask learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 52(9), 5923–5936 (2014)

    Article  Google Scholar 

  38. Fang, L., Li, S., Kang, X., Benediktsson, J.A.: Spectralspatial hyperspectral image classification via multiscale adaptive sparse representation. IEEE Trans. Geosci. Remote Sens. 52(12), 7738–7749 (2014)

    Article  Google Scholar 

  39. Zheng, X., Sun, X., Fu, K., Wang, H.: Automatic annotation of satellite images via multifeature joint sparse coding with spatial relation constraint. IEEE Geosci. Remote Sens. Lett. 10(4), 652–656 (2013)

    Article  Google Scholar 

  40. Camps-Valls, G., Bruzzone, L.: Kernel-based methods for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 43(6), 1351–1362 (2005)

    Article  Google Scholar 

  41. Plaza, A., Benediktsson, J.A., Boardman, J.W., Brazile, J., Bruzzone, L., Camps-Valls, G.: Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 113, S110–S122 (2009)

    Article  Google Scholar 

  42. Su, S., Ming, Y.: Hyperspectral linage classification method based on watershed segmentation and sparse representation. Comput. Sci. 43(2), 89–94 (2016)

    Google Scholar 

  43. Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  44. Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: Advances in neural information processing systems, pp. 801–808 (2007)

  45. Engan, K., Aase, S.O., Husoy, J.H.: Method of optimal directions for frame design. In: Proceedings of 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, pp. 2443–2446 (1999)

  46. Tadjudin, S., Landgrebe, D.A.: Covariance estimation with limited training samples. IEEE Trans. Geosci. Remote Sens. 37(4), 2113–2118 (1999)

    Article  Google Scholar 

  47. Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Chanussot, J., Angulo, J., Fauvel, M.: Classification of hyperspectral data using support vector machines and adaptive neighborhoods. In: Proceedings of 6th EARSeL SIG IS Workshop, pp. 1–6 (2009)

  48. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, pp. 119–131. Wiley, Hoboken (2001)

    MATH  Google Scholar 

Download references

Acknowledgements

This work is supported by National Natural Science Foundation of China (61432008, 61272222, and 61603193) and Natural Science Foundation of Jiangsu Province (BK20171479, BK20161020, BK20161560).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, Nanjing, 210023, People’s Republic of China

    Huimin Zhang, Ming Yang, Wanqi Yang & Jing Lv

Authors
  1. Huimin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wanqi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jing Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huimin Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Yang, M., Yang, W. et al. Spatial-aware hyperspectral image classification via multifeature kernel dictionary learning. Int J Data Sci Anal 7, 115–129 (2019). https://doi.org/10.1007/s41060-018-0115-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41060-018-0115-0

Keywords

Search

Navigation