Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Hyperspectral image classification based on clustering dimensionality reduction and multi-scale feature fusion

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Hyperspectral images (HSI) contain rich ground object information, which has great potential in classification. However, the large amount of data and noise also pose a challenge to HSI classification. In this paper, a new framework based on band selection and multi-scale structure features is proposed, which mainly consists of the following steps. Firstly, the spectral dimension of the HSI is reduced with the clustering average method based on information divergence. Secondly, the detailed multi-scale structure features of HSI are extracted by using multi-parameter relative total variation. Thirdly, in order to reduce noise and highlight structural features, bilateral filtering is used to fine-tune the extracted structural features. Finally, the improved quantum particle swarm optimization algorithm is proposed to optimize the parameters of SVM. A lot of experiment results on two hyperspectral datasets show that the proposed method performs better than several state-of-the-art methods.

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
$34.99 /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 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
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The data can be available online http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes

Notes

  1. http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.

  2. https://github.com/Songxiaonan-web.

References

  1. Goetz, A.F.H., Vane, G., Solomon, J.E., Rock, B.N.: Imaging spectrometry for earth remote sensing. Science 228(4704), 1147–1153 (1985). https://doi.org/10.1126/science.228.4704.1147

    Article  Google Scholar 

  2. Bioucas-Dias, J.M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N.M., Chanussot, J.: Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci. Remote Sens. Mag. (2013). https://doi.org/10.1109/MGRS.2013.2244672

    Article  Google Scholar 

  3. Zhang, X., Sun, Y., Shang, K., Zhang, L., Wang, S.: Crop classification based on feature band set construction and object-oriented approach using hyperspectral images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(9), 4117–4128 (2016). https://doi.org/10.1109/JSTARS.2016.2577339

    Article  Google Scholar 

  4. Zhang, L., Zhang, L., Tao, D., Huang, X., Du, B.: Hyperspectral remote sensing image subpixel target detection based on supervised metric learning. IEEE Trans. Geosci. Remote Sens. 52(8), 4955–4965 (2014). https://doi.org/10.1109/TGRS.2013.2286195

    Article  Google Scholar 

  5. Hörig, B., Kühn, F., Oschütz, F., Lehmann, F.: HyMap hyperspectral remote sensing to detect hydrocarbons. Int. J. Remote Sens. 22(8), 1413–1422 (2001). https://doi.org/10.1080/01431160120909

    Article  Google Scholar 

  6. Qin, Q., Zhang, Z., Chen, L., Wang, N., Zhang, C.: Oil and gas reservoir exploration based on hyperspectral remote sensing and super-low-frequency electromagnetic detection. J. Appl. Remote Sens. 10(1), 016017 (2016). https://doi.org/10.1117/1.jrs.10.016017

    Article  Google Scholar 

  7. Yang, C., Tan, Y., Bruzzone, L., Lu, L., Guan, R.: Discriminative feature metric learning in the affinity propagation model for band selection in hyperspectral images. Remote Sens. 9(8), 782 (2017). https://doi.org/10.3390/rs9080782

    Article  Google Scholar 

  8. Ul Haq, Q.S., Tao, L., Sun, F., Yang, S.: 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). https://doi.org/10.1109/TGRS.2011.2172617

    Article  Google Scholar 

  9. Li, X., Chen, M., Wang, Q.: Quantifying and detecting collective motion in crowd scenes. IEEE Trans. Image Process. 29, 5571–5583 (2020)

    Article  MATH  Google Scholar 

  10. Licciardi, G., Marpu, P.R., Chanussot, J., Benediktsson, J.A.: Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geosci. Remote Sens. Lett. 9(3), 447–451 (2012). https://doi.org/10.1109/LGRS.2011.2172185

    Article  Google Scholar 

  11. Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.: Hyperspectral image classification with independent component discriminant analysis. IEEE Trans. Geosci. Remote Sens. 49(12), 4865–4876 (2011). https://doi.org/10.1109/TGRS.2011.2153861

    Article  Google Scholar 

  12. Rellier, G., Descombes, X., Falzon, F., Zerubia, J.: Texture feature analysis using a Gauss-Markov model in hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 42(7), 1543–1551 (2004). https://doi.org/10.1109/TGRS.2004.830170

    Article  MATH  Google Scholar 

  13. Wang, L., Wang, X., Wang, Q.: Using 250-m MODIS data for enhancing spatiotemporal fusion by sparse representation. Photogramm. Eng. Remote Sens. 86(6), 383–392 (2020). https://doi.org/10.14358/PERS.86.6.383

    Article  Google Scholar 

  14. Du, P., Bai, X., Tan, K., Xue, Z., Samat, A., Xia, J., Li, E., Su, H., Liu, W.: Advances of four machine learning methods for spatial data handling: a review. J. Geovis. Spat. Anal. 4(1), 1–25 (2020). https://doi.org/10.1007/s41651-020-00048-5

    Article  Google Scholar 

  15. Marpu, P.R., Pedergnana, M., Dalla Mura, M., Benediktsson, J.A., Bruzzone, L.: Automatic generation of standard deviation attribute profiles for spectral-spatial classification of remote sensing data. IEEE Geosci. Remote Sens. Lett. 10(2), 293–297 (2013). https://doi.org/10.1109/LGRS.2012.2203784

    Article  Google Scholar 

  16. Hadoux, X., Jay, S., Rabatel, G., Gorretta, N.: A spectral-spatial approach for hyperspectral image classification using spatial regularization on supervised score image. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 2361–2369 (2015). https://doi.org/10.1109/JSTARS.2014.2347414

    Article  Google Scholar 

  17. Leng, J., Li, T., Bai, G., Dong, Q., Dong, H.: Cube-CNN-SVM: a novel hyperspectral image classification method. In: Proceedings—2016 IEEE 28th International Conference on Tools with Artificial Intelligence, ICTAI 2016 (2017). https://doi.org/10.1109/ICTAI.2016.0155

  18. Jamshidpour, N., Homayouni, S., Safari, A.: Graph-based semi-supervised hyperspectral image classification using spatial information. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences—ISPRS Archives, vol. 42 (2017). https://doi.org/10.5194/isprs-archives-XLII-4-W4-91-2017

  19. Liao, J., Wang, L., Hao, S.: Hyperspectral image classification based on adaptive optimisation of morphological profile and spatial correlation information. Int. J. Remote Sens. 39(23), 9159–9180 (2018). https://doi.org/10.1080/01431161.2018.1508913

    Article  Google Scholar 

  20. Mu, C., Liu, J., Liu, Y., Liu, Y.: Hyperspectral image classification based on active learning and spectral-spatial feature fusion using spatial coordinates. IEEE Access 8, 6768–6781 (2020). https://doi.org/10.1109/ACCESS.2019.2963624

    Article  Google Scholar 

  21. Chen, M., Li, X.: Robust matrix factorization with spectral embedding. IEEE Trans. Neural Netw. Learn. Syst. 32(12), 5698–5707 (2020)

    Article  MathSciNet  Google Scholar 

  22. Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., Plaza, A.: Advances in hyperspectral image and signal processing: a comprehensive overview of the state of the art. IEEE Geosci. Remote Sens. Mag. 5(4), 37–78 (2017)

    Article  Google Scholar 

  23. Yuan, Q., Zhang, L., Shen, H.: Hyperspectral image denoising employing a spectral-spatial adaptive total variation model. IEEE Trans. Geosci. Remote Sens. 50(10), 3660–3677 (2012)

    Article  Google Scholar 

  24. Bhattacharya, S., Das, S., Routray, A.: Graph manifold clustering based band selection for hyperspectral face recognition. In: European Signal Processing Conference, vol. 2018-September (2018). https://doi.org/10.23919/EUSIPCO.2018.8553006

  25. Li, Q., Wang, Q., Li, X.: An efficient clustering method for hyperspectral optimal band selection via shared nearest neighbor. Remote Sens. 11(3), 350 (2019). https://doi.org/10.3390/rs11030350

    Article  Google Scholar 

  26. Hayati, S., Saryazdi, S., Nezamabadi-pour, H.: Structure-texture image decomposition for content-based image retrieval. Nashriyyah-i Muhandisi-i Barq va Muhandisi-i Kampyutar-i Iran 32, 115 (2013)

    Google Scholar 

  27. Wu, X., Zheng, J., Wu, C., Cai, Y.: Variational structure-texture image decomposition on manifolds. Signal Process. 93(7), 1773–1784 (2013)

    Article  Google Scholar 

  28. Chen, M., Li, X.: Concept factorization with local centroids. IEEE Trans. Neural Netw. Learn. Syst. 32(11), 5247–5253 (2020)

    Article  MathSciNet  Google Scholar 

  29. Cannone, M., Miyakawa, T.: Mathematical Foundation of Turbulent Viscous Flows, vol. 1871, p. 252. Springer (2006)

  30. Aujol, J.F., Gilboa, G., Chan, T., Osher, S.: Structure-texture image decomposition-modeling, algorithms, and parameter selection. Int. J. Comput. Vis. 67(1), 111–136 (2006)

    Article  MATH  Google Scholar 

  31. Xu, L., Yan, Q., Xia, Y., Jia, J.: Structure extraction from texture via relative total variation. ACM Trans. Graph. 31, 1–10 (2012). https://doi.org/10.1145/2366145.2366158

    Article  Google Scholar 

  32. Cai, X., Cui, Z.: Hungry particle swarm optimization. ICIC Express Lett. 4(3), 1071–1076 (2010)

    Google Scholar 

  33. Sun, J., Feng, B., Xu, W.: Particle swarm optimization with particles having quantum behavior. In: Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004, vol. 1 (2004). https://doi.org/10.1109/cec.2004.1330875

  34. Sun, J., Fang, W., Wu, X., Palade, V., Xu, W.: Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection. Evolut. Comput. 20(3), 349–393 (2012)

    Article  Google Scholar 

  35. Li, X., Chen, M., Nie, F., Wang, Q.: A multiview-based parameter free framework for group detection. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

  36. Chen, M., Li, X.: Concept factorization with local centroids. IEEE Trans. Neural Netw. Learn. Syst. 32(11), 5247–5253 (2021). https://doi.org/10.1109/TNNLS.2020.3027068

    Article  MathSciNet  Google Scholar 

  37. Li, X., Chen, M., Nie, F., Wang, Q.: Locality adaptive discriminant analysis. In: IJCAI, pp. 2201–2207 (2017)

  38. Wang, Q., Li, Q., Li, X.: Hyperspectral band selection via adaptive subspace partition strategy. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12(12), 4940–4950 (2019). https://doi.org/10.1109/JSTARS.2019.2941454

    Article  Google Scholar 

  39. Chen, M., Li, X.: Robust matrix factorization with spectral embedding. IEEE Trans. Neural Netw. Learn. Syst. 32(12), 5698–5707 (2021). https://doi.org/10.1109/TNNLS.2020.3027351

    Article  MathSciNet  Google Scholar 

  40. Duan, P., Kang, X., Li, S., Ghamisi, P.: Noise-robust hyperspectral image classification via multi-scale total variation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12(6), 1948–1962 (2019). https://doi.org/10.1109/JSTARS.2019.2915272

    Article  Google Scholar 

  41. Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on geoscience and remote sensing. 42(8), 1778–1790 (2004)

  42. Bau, T.C., Sarkar, S., Healey, G.: Hyperspectral region classification using a three-dimensional Gabor filterbank. IEEE Trans. Geosci. Remote Sens. 48(9), 3457–3464 (2010). https://doi.org/10.1109/TGRS.2010.2046494

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the editor and reviewers.

Funding

This research was funded by the National Natural Science Foundation of China under Grant 62002286 and the Strategic Priority Science and Technology Project of Chinese Academy of Sciences(No. XDA23000000).

Author information

Author notes

  1. Xiaonan Song and Jing Zhang have contributed equally to this work.

Authors and Affiliations

  1. College of Computer Science and Technology, Xi’an Shiyou University, Yanta District, Xi’an, 710000, Shaanxi Province, China

    Cailing Wang, Xiaonan Song & Jing Zhang

Authors
  1. Cailing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaonan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Cailing Wang contributed to methodology and funding acquisition; Xiaonan Song contributed to data acquisition, experimental analysis, writing original draft, and writing—review and editing; Jing Zhang performed revision and proofreading.

Corresponding author

Correspondence to Cailing Wang.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, C., Song, X. & Zhang, J. Hyperspectral image classification based on clustering dimensionality reduction and multi-scale feature fusion. Machine Vision and Applications 33, 90 (2022). https://doi.org/10.1007/s00138-022-01340-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00138-022-01340-8

Keywords

Search

Navigation