Skip to main content
SpringerLink
Log in

Hyperspectral Image Feature Extraction Using Maclaurin Series Function Curve Fitting

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Most of existing spectral-based feature extraction algorithms have gained increasing attention in hyperspectral image classification tasks. However, only original spectral is difficult to well represent or reveal intrinsic geometry structure of the image. In this paper, we construct the new features for each spectral response curve of hyperspectral image pixels, and then proposed a novel unsupervised nonlinear feature extraction algorithm that focuses on curve fitting and label-based discrimination analysis framework. In the algorithm, the coefficients of the fitted Maclaurin series function are considered as new extracted features in order to better capture the intrinsic geometrical nature of spectral response curves. Moreover, the algorithm can utilize the reflectance coefficients information of spectral response curves which has not been solved by many other statistical analysis based methods. The maximum likelihood classification results on two real-world hyperspectral image datasets have demonstrated the superiority of the proposed algorithm in image classification tasks.

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

Similar content being viewed by others

References

  1. David L (2002) Hyperspectral image data analysis as a high dimensional signal processing problem. IEEE Signal Process Mag 19(1):17–28

    Article  MathSciNet  Google Scholar 

  2. Bioucas-Dias JM, Plaza A, Camps-Valls G et al (2013) Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci Remote Sens Mag 1(2):6–36

    Article  Google Scholar 

  3. Tan K, Li E, Du Q et al (2014) Hyperspectral image classification using band selection and morphological profiles. IEEE J Select Top Appl Earth Observ Remote Sens 7(1):40–48

    Article  Google Scholar 

  4. Van der Meer FD, Van der Werff HMA, Van Ruitenbeek FJ et al (2012) Multi-and hyperspectral geologic remote sensing: a review. Int J Appl Earth Obs Geoinf 14(1):112–128

    Article  Google Scholar 

  5. Du P, Xia J, Zhang W et al (2012) Multiple classifier system for remote sensing image classification: a review. Sensors 12(4):4764–4792

    Article  Google Scholar 

  6. Hosseini SA, Ghassemian H (2016) Hyperspectral data feature extraction using rational function curve fitting. Int J Pattern Recognit Artif Intell 30(01):1650001. https://doi.org/10.1142/S0218001416500014

    Article  MathSciNet  Google Scholar 

  7. Hosseini SA, Ghassemian H (2016) Rational function approximation for feature reduction in hyperspectral data. Remote Sens Lett 7(2):101–110

    Article  Google Scholar 

  8. Imani M, Ghassemian H (2017) High-dimensional image data feature extraction by double discriminant embedding. Pattern Anal Appl 20(2):473–484

    Article  MathSciNet  Google Scholar 

  9. Imani M, Ghassemian H (2016) Binary coding based feature extraction in remote sensing high dimensional data. Inf Sci 342:191–208

    Article  Google Scholar 

  10. Jia X, Kuo BC, Crawford MM (2013) Feature mining for hyperspectral image classification. Proc IEEE 101(3):676–697

    Article  Google Scholar 

  11. Maji P, Garai P (2013) Fuzzy-rough simultaneous attribute selection and feature extraction algorithm. IEEE Trans Cybern 43(4):1166–1177

    Article  Google Scholar 

  12. Li S, Qiu J, Yang X et al (2014) A novel approach to hyperspectral band selection based on spectral shape similarity analysis and fast branch and bound search. Eng Appl Artif Intell 27:241–250

    Article  Google Scholar 

  13. Esfandian N, Razzazi F, Behrad A (2012) A clustering based feature selection method in spectro-temporal domain for speech recognition. Eng Appl Artif Intell 25(6):1194–1202

    Article  Google Scholar 

  14. Dernoncourt D, Hanczar B, Zucker JD (2014) Analysis of feature selection stability on high dimension and small sample data. Comput Stat Data Anal 71:681–693

    Article  MathSciNet  MATH  Google Scholar 

  15. Zhang L, Zhong Y, Huang B et al (2007) Dimensionality reduction based on clonal selection for hyperspectral imagery. IEEE Trans Geosci Remote Sens 45(12):4172–4186

    Article  Google Scholar 

  16. Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(6):417–441

    Article  MATH  Google Scholar 

  17. Liao W, Pizurica A, Scheunders P et al (2013) Semisupervised local discriminant analysis for feature extraction in hyperspectral images. IEEE Trans Geosci Remote Sens 51(1):184–198

    Article  Google Scholar 

  18. Plaza A, Martinez P, Plaza J et al (2005) Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations. IEEE Trans Geosci Remote Sens 43(3):466–479

    Article  Google Scholar 

  19. Journaux L, Tizon X, Foucherot I et al (2006) Dimensionality reduction techniques: an operational comparison on multispectral satellite images using unsupervised clustering. In: Signal processing symposium, NORSIG 2006. Proceedings of the 7th Nordic. IEEE, pp 242–245

  20. Fauvel M, Chanussot J, Benediktsson J A (2009) Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas. EURASIP J Adv Signal Process, Article ID 783194. https://doi.org/10.1155/2009/783194

  21. Zhong Y, Zhang L, Huang B et al (2006) An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery. IEEE Trans Geosci Remote Sens 44(2):420–431

    Article  Google Scholar 

  22. Villa A, Chanussot J, Benediktsson JA et al (2013) Unsupervised methods for the classification of hyperspectral images with low spatial resolution. Pattern Recogn 46(6):1556–1568

    Article  Google Scholar 

  23. Chang CI, Ren H (2000) An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery. IEEE Trans Geosci Remote Sens 38(2):1044–1063

    Article  Google Scholar 

  24. Kuo BC, Landgrebe DA (2004) Nonparametric weighted feature extraction for classification. IEEE Trans Geosci Remote Sens 42(5):1096–1105

    Article  Google Scholar 

  25. Landgrebe DA (2005) Signal theory methods in multispectral remote sensing. Wiley, New York

    Google Scholar 

  26. Mika S, Ratsch G, Weston J et al (1999) Fisher discriminant analysis with kernels. In: Neural networks for signal processing IX, 1999. Proceedings of the 1999 IEEE signal processing society workshop. IEEE, pp 41–48

  27. Baudat G, Anouar F (2000) Generalized discriminant analysis using a kernel approach. Neural Comput 12(10):2385–2404

    Article  Google Scholar 

  28. Cai D, He X, Han J (2007) Semi-supervised discriminant analysis. In: IEEE 11th international conference on computer vision-ICCV07, Rio de Janeiro, Brazil, pp 1–7

  29. Chen S, Zhang D (2011) Semisupervised dimensionality reduction with pairwise constraints for hyperspectral image classification. IEEE Geosci Remote Sens Lett 8(2):369–373

    Article  Google Scholar 

  30. Sugiyama M, Ide T, Nakajima S et al (2010) Semi-supervised local Fisher discriminant analysis for dimensionality reduction. Mach Learn 78(1):35–61

    Article  MathSciNet  Google Scholar 

  31. He X, Cai D, Yan S et al (2005) Neighborhood preserving embedding. In: 10th IEEE international conference on computer vision-ICCV 2005, vol 2, pp 1208–1213

  32. He X, Niyogi P (2004) Locality preserving projections. In: Thrun S, Saul L, Scholkopf B (eds) Advances in neural information processing systems, vol 16. MIT Press, Cambridge, MA, pp 153–160

  33. Zhang T, Yang J, Zhao D et al (2007) Linear local tangent space alignment and application to face recognition. Neurocomputing 70(7):1547–1553

    Article  Google Scholar 

  34. He X, Cai D, Han J (2008) Learning a maximum margin subspace for image retrieval. IEEE Trans Knowl Data Eng 20(2):189–201

    Article  Google Scholar 

  35. Li L, Ge H, Gao J (2017) A spectral-spatial kernel-based method for hyperspectral imagery classification. Adv Space Res 59(4):954–967

    Article  Google Scholar 

  36. Gao J, Xu L (2016) A novel spatial analysis method for remote sensing image classification. Neural Process Lett 43(3):805–821

    Article  Google Scholar 

  37. Gao J, Xu L, Shen J et al (2015) A novel information transferring approach for the classification of remote sensing images. EURASIP J Adv Signal Process 2015(1):38

    Article  Google Scholar 

  38. Gao J, Xu L, Huang F (2016) A spectral-textural kernel-based classification method of remotely sensed images. Neural Comput Appl 27(2):431–446

    Article  Google Scholar 

  39. Gao J, Xu L (2015) An efficient method to solve the classification problem for remote sensing image. AEU Int J Electron Commun 69(1):198–205

    Article  Google Scholar 

  40. Gao J, Xu L, Shi A et al (2014) A kernel-based block matrix decomposition approach for the classification of remotely sensed images. Appl Math Comput 228:531–545

    MathSciNet  MATH  Google Scholar 

  41. Hosseini A, Ghassemian H (2012) Classification of hyperspectral and multispectral images by using fractal dimension of spectral response curve. In: Electrical engineering (ICEE), 2012 20th Iranian conference on IEEE, pp 1452–1457

  42. Hosseini S A, Ghassemian H (2013) A new hyperspectral image classification approach using fractal dimension of spectral response curve. In: Electrical engineering (ICEE), 2013 21st Iranian conference on IEEE, pp 1–6

  43. Caglar H, Akansu AN (1993) A generalized parametric PR-QMF design technique based on Bernstein polynomial approximation. IEEE Trans Signal Process 41(7):2314–2321

    Article  MATH  Google Scholar 

  44. Davis PJ (1975) Interpolation and approximation. Courier Corporation, Mineola

    MATH  Google Scholar 

  45. Purdue Research Foundation, Hyperspectral images by multiSpec (2015). https://engineering.purdue.edu/~biehl/MultiSpec/

  46. Universidad-del-Pais-Vasco, Hyperspectral remote sensing scenes (2014). http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes

Download references

Acknowledgements

This work is supported by the Graduate Innovation Foundation of Jiangsu Province under Grant No. KYLX16_0781, the 111 Project under Grant No. B12018, and PAPD of Jiangsu Higher Education Institutions, China.

Author information

Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Ministry of Education, Wuxi, 214122, China

    Li Li & Hongwei Ge

  2. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China

    Li Li & Hongwei Ge

  3. School of Medical Information Engineering, Jining Medical University, Rizhao, 276826, Shandong, China

    Jianqiang Gao

  4. School of Science, Jiangnan University, Wuxi, 214122, China

    Yixin Zhang

Authors
  1. Li Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongwei Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianqiang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yixin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongwei Ge.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, L., Ge, H., Gao, J. et al. Hyperspectral Image Feature Extraction Using Maclaurin Series Function Curve Fitting. Neural Process Lett 49, 357–374 (2019). https://doi.org/10.1007/s11063-018-9825-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-018-9825-5

Keywords

Mathematics Subject Classification

Search

Navigation