Skip to main content
SpringerLink
Log in

GPU-based fast hyperspectral image classification using joint sparse representation with spectral consistency constraint

  • Special Issue Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Due to the fact that neighboring hyperspectral pixels often belong to the same class with high probability, spatial correlation between pixels has been widely used in hyperspectral image classification. In this paper, a novel joint sparse representation classifier with spectral consistency constraint (JSRC-SCC) is proposed. Specifically, to efficiently exploit contextual structure information, a local adaptive weighted average value is reallocated as the central pixel of a window through spatial filtering, and then, representation coefficients are estimated by the joint sparse representation model, which is imposed by the spectral consistency constraint under \(\textit{l}_1\)-minimization. For the purpose of fast classification, graphics processing units are adopted to accelerate this model. Experimental results on two classical hyperspectral image data sets demonstrate the proposed method can not only produce satisfying classification performance, but also shorten the computational time significantly.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

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

Similar content being viewed by others

References

  1. Tarabalka, Y., Fauvel, M., Chanussot, J., Benediktsson, J.A.: SVM- and MRF-based method for accurate classification of hyperspectral images. IEEE Geosci. Remote Sens. Lett. 7(4), 736–740 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Guo, X., Huang, X., Zhang, L.F., Zhang, L.P., Plaza, A., Benediktsson, J.A.: Support tensor machines for classification of hyperspectral remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 54(6), 3248–3264 (2016)

    Article  Google Scholar 

  4. Khodadadzadeh, M., Li, J., Plaza, A., Ghassemian, H., Bioucis-Dias, J.M., Li, X.: Spectral-spatial classification of hyperspectral data using local and global probablities for mixed pixel characterization. IEEE Trans. Geosci. Remote Sens. 52(10), 6298–6314 (2014)

    Article  Google Scholar 

  5. Moser, G., Serpico, S.B.: Combining support vector machines and markov random fields in an integrated framework for contextual image classification. IEEE Trans. Geosci. Remote Sens. 51(5), 2734–2754 (2013)

    Article  Google Scholar 

  6. Jia, S., Hu, J., Xie, Y., Shen, L.L., Jia, X.P., Li, Q.Q.: Gabor cube selection based multitask joint sparse representation for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 54(6), 3174–3187 (2016)

    Article  Google Scholar 

  7. Huang, X., Guan, X.H., Benediktsson, J.A., Zhang, L.P., Li, J., Plaza, A., Mura, M.D.: Mutiple morphological profiles from multicomponent-base images for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(12), 4653–4669 (2014)

    Article  Google Scholar 

  8. Hu, W., Huang, Y.Y., Li, W., Zhang, F., Li, H.C.: Deep convolutional neural networks for hyperspectral image classification. J. Sens. (2015). https://doi.org/10.1155/2015/258619

    Article  Google Scholar 

  9. Li, W., Wu, G.D., Zhang, F., Du, Q.: Hyperspectral image classification using deep pixel-pair features. IEEE Trans. Geosci. Remote Sens. 55(2), 844–853 (2017)

    Article  Google Scholar 

  10. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)

    Article  Google Scholar 

  11. Xu, Y., Du, Q., Li, W., Chen, C., Younan, N.H.: Nonlinear classification of multispectral imagery using representation-based classifiers. Remote Sens. 9(662), 1–12 (2017)

    Google Scholar 

  12. Sami ul Haq, Q., Tao, L.M., Sun, F.C., Yang, S.Q.: A fast and robust sparse approach for hyperspectral data classification using a few labeled samples. IEEE Trans. Geosci. Remote Sens. 50(6), 2287–2302 (2012)

    Article  Google Scholar 

  13. Liu, G.C., Lin, Z.C., Yan, S.C., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 171–184 (2013)

    Article  Google Scholar 

  14. Pan, L., Li, H.-C., Li, W., Chen, X.-D., Wu, G.-N., Du, Q.: Discriminant analysis of hyperspectral imagery using fast kernel sparse and low-rank graph. IEEE Trans. Geosci. Remote Sens. 55(11), 6085–6098 (2017)

    Article  Google Scholar 

  15. Sun, W.W., Yang, G., Du, B., Zhang, L.F., Zhang, L.P.: A sparse and low-rank near-isometric linear embedding method for feature exraction in hyperspectral imagery classification. IEEE Trans. Geosci. Remote Sens. 55(7), 4032–4046 (2017)

    Article  Google Scholar 

  16. Pan, L., Li, H.-C., Meng, H., Li, W., Du, Q., Emery, W.J.: Hyperspectral image classification via low-rank and sparse representation with spectral consistency constraint. IEEE Geosci. Remote Sens. Lett. 14(11), 2117–2121 (2017)

    Article  Google Scholar 

  17. 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 

  18. Zhang, H.Y., Li, J.Y., Huang, Y.C., Zhang, L.P.: A nonlocal weighted joint sparse representation classification method for hyperspectral imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(6), 2057–2066 (2014)

    Article  Google Scholar 

  19. Chen, C., Chen, N., Peng, J.T.: Nearest regularized joint sparse representation for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 13(3), 424–428 (2016)

    Google Scholar 

  20. Peng, J.T., Du, Q.: Robust joint sparse representation based on maximum correntropy criterion for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 55(12), 7152–7164 (2017)

    Article  Google Scholar 

  21. Bian, X.Y., Chen, C., Xu, Y., Du, Q.: Robust hyperspectral image classification by multi-layer spatial-spectral sparse representation. Remote Sens. 8(985), 1–24 (2016)

    Google Scholar 

  22. Li, W., Du, Q.: Joint within-class collaborative representation for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(6), 2200–2208 (2014)

    Article  Google Scholar 

  23. Zhang, F., Hu, C., Li, W., Hu, W., Li, H.-C.: Accelerating time-domain SAR raw data simulation for large areas using multi-GPUs. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(9), 3956–3966 (2014)

    Article  Google Scholar 

  24. Torti, E., Fontanella, A., Plaza, A.: Parallel real-time virtual dimensionality estimation for hyperspectral images. J. Real Time Image Process. (2017). https://doi.org/10.1007/s1154-017-0703-6

    Article  Google Scholar 

  25. Wu, Z.B., Wang, Q.C., Plaza, A., Li, J., Liu, J.J., Wei, Z.H.: Parallel implementation of sparse representation classifiers for hyperspectral imagery on GPUs. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 2912–2925 (2015)

    Article  Google Scholar 

  26. Wu, Z.B., Wang, Q.C., Plaza, A., Li, J., Sun, L., Wei, Z.H.: Parallel spatial-spectral hyperspectral image classification with sparse representation and markov random fields on GPUs. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 2926–2938 (2015)

    Article  Google Scholar 

  27. Zhang, F., Hu, C., Li, W., Hu, W., Wang, P.B., Li, H.-C.: A deep collaborative computing based SAR raw data simulation on multiple CPU/GPU platform. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(2), 387–399 (2017)

    Article  Google Scholar 

  28. Wang, J.J., Yang, J.C., Yu, K., Lv, F.J., Huang, T., Gong, Y.H.: Locality-constrained linear coding for image classification. In: Proceedings on CVPR2010, pp. 3360–3367 (2010)

  29. Li, W., Tramel, E.W., Prasad, S., Fowler, J.E.: Nearest regularized subspace for hyperspectral classification. IEEE Trans. Geosci. Remote Sens. 52(1), 477–489 (2014)

    Article  Google Scholar 

  30. Beaton, A.E., Tukey, J.W.: The fitting of power series, meaning polynomials, illustrated on band-spectroscopic data. Technometrics 16, 147–185 (1974)

    Article  Google Scholar 

  31. Cai, J.-F., Candés, E.J., Shen, Z.: A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 24(4), 1956–1982 (2010)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61371165 and the Frontier Intersection Basic Research Project for the Central Universities under Grant A0920502051714-5.

Author information

Authors and Affiliations

  1. The School of Information Science & Technology, Southwest Jiaotong University, Chengdu, 610031, China

    Lei Pan, Heng-Chao Li & Xiang-Dong Chen

  2. The College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China

    Jun Ni

  3. The Center of Research in Computer Vision, University of Central Florida, Orlando, FL, 32816, USA

    Chen Chen

  4. The Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, 39762, USA

    Qian Du

Authors
  1. Lei Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Heng-Chao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiang-Dong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qian Du
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Pan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pan, L., Li, HC., Ni, J. et al. GPU-based fast hyperspectral image classification using joint sparse representation with spectral consistency constraint. J Real-Time Image Proc 15, 463–475 (2018). https://doi.org/10.1007/s11554-018-0775-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-018-0775-y

Keywords

Search

Navigation