Advertisement

Neural Processing Letters

, Volume 49, Issue 1, pp 357–374 | Cite as

Hyperspectral Image Feature Extraction Using Maclaurin Series Function Curve Fitting

  • Li Li
  • Hongwei GeEmail author
  • Jianqiang Gao
  • Yixin Zhang
Article

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.

Keywords

Hyperspectral image Feature extraction Spectral response curve Curve fitting Classification 

Mathematics Subject Classification

68T10 68U10 

Notes

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.

References

  1. 1.
    David L (2002) Hyperspectral image data analysis as a high dimensional signal processing problem. IEEE Signal Process Mag 19(1):17–28MathSciNetGoogle Scholar
  2. 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):6–36Google Scholar
  3. 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):40–48Google Scholar
  4. 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):112–128Google Scholar
  5. 5.
    Du P, Xia J, Zhang W et al (2012) Multiple classifier system for remote sensing image classification: a review. Sensors 12(4):4764–4792Google Scholar
  6. 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 MathSciNetGoogle Scholar
  7. 7.
    Hosseini SA, Ghassemian H (2016) Rational function approximation for feature reduction in hyperspectral data. Remote Sens Lett 7(2):101–110Google Scholar
  8. 8.
    Imani M, Ghassemian H (2017) High-dimensional image data feature extraction by double discriminant embedding. Pattern Anal Appl 20(2):473–484MathSciNetGoogle Scholar
  9. 9.
    Imani M, Ghassemian H (2016) Binary coding based feature extraction in remote sensing high dimensional data. Inf Sci 342:191–208Google Scholar
  10. 10.
    Jia X, Kuo BC, Crawford MM (2013) Feature mining for hyperspectral image classification. Proc IEEE 101(3):676–697Google Scholar
  11. 11.
    Maji P, Garai P (2013) Fuzzy-rough simultaneous attribute selection and feature extraction algorithm. IEEE Trans Cybern 43(4):1166–1177Google Scholar
  12. 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 27:241–250Google Scholar
  13. 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):1194–1202Google Scholar
  14. 14.
    Dernoncourt D, Hanczar B, Zucker JD (2014) Analysis of feature selection stability on high dimension and small sample data. Comput Stat Data Anal 71:681–693MathSciNetzbMATHGoogle Scholar
  15. 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):4172–4186Google Scholar
  16. 16.
    Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(6):417–441zbMATHGoogle Scholar
  17. 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):184–198Google Scholar
  18. 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):466–479Google Scholar
  19. 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 242–245Google Scholar
  20. 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. 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):420–431Google Scholar
  22. 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):1556–1568Google Scholar
  23. 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):1044–1063Google Scholar
  24. 24.
    Kuo BC, Landgrebe DA (2004) Nonparametric weighted feature extraction for classification. IEEE Trans Geosci Remote Sens 42(5):1096–1105Google Scholar
  25. 25.
    Landgrebe DA (2005) Signal theory methods in multispectral remote sensing. Wiley, New YorkGoogle Scholar
  26. 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 41–48Google Scholar
  27. 27.
    Baudat G, Anouar F (2000) Generalized discriminant analysis using a kernel approach. Neural Comput 12(10):2385–2404Google Scholar
  28. 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 1–7Google Scholar
  29. 29.
    Chen S, Zhang D (2011) Semisupervised dimensionality reduction with pairwise constraints for hyperspectral image classification. IEEE Geosci Remote Sens Lett 8(2):369–373Google Scholar
  30. 30.
    Sugiyama M, Ide T, Nakajima S et al (2010) Semi-supervised local Fisher discriminant analysis for dimensionality reduction. Mach Learn 78(1):35–61MathSciNetGoogle Scholar
  31. 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 1208–1213Google Scholar
  32. 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 153–160Google Scholar
  33. 33.
    Zhang T, Yang J, Zhao D et al (2007) Linear local tangent space alignment and application to face recognition. Neurocomputing 70(7):1547–1553Google Scholar
  34. 34.
    He X, Cai D, Han J (2008) Learning a maximum margin subspace for image retrieval. IEEE Trans Knowl Data Eng 20(2):189–201Google Scholar
  35. 35.
    Li L, Ge H, Gao J (2017) A spectral-spatial kernel-based method for hyperspectral imagery classification. Adv Space Res 59(4):954–967Google Scholar
  36. 36.
    Gao J, Xu L (2016) A novel spatial analysis method for remote sensing image classification. Neural Process Lett 43(3):805–821Google Scholar
  37. 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):38Google Scholar
  38. 38.
    Gao J, Xu L, Huang F (2016) A spectral-textural kernel-based classification method of remotely sensed images. Neural Comput Appl 27(2):431–446Google Scholar
  39. 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):198–205Google Scholar
  40. 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 228:531–545MathSciNetzbMATHGoogle Scholar
  41. 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 1452–1457Google Scholar
  42. 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 1–6Google Scholar
  43. 43.
    Caglar H, Akansu AN (1993) A generalized parametric PR-QMF design technique based on Bernstein polynomial approximation. IEEE Trans Signal Process 41(7):2314–2321zbMATHGoogle Scholar
  44. 44.
    Davis PJ (1975) Interpolation and approximation. Courier Corporation, MineolazbMATHGoogle Scholar
  45. 45.
    Purdue Research Foundation, Hyperspectral images by multiSpec (2015). https://engineering.purdue.edu/~biehl/MultiSpec/
  46. 46.
    Universidad-del-Pais-Vasco, Hyperspectral remote sensing scenes (2014). http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes

Copyright information

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

Authors and Affiliations

  • Li Li
    • 1
    • 2
  • Hongwei Ge
    • 1
    • 2
    Email author
  • Jianqiang Gao
    • 3
  • Yixin Zhang
    • 4
  1. 1.Key Laboratory of Advanced Process Control for Light IndustryJiangnan University, Ministry of EducationWuxiChina
  2. 2.School of Internet of Things EngineeringJiangnan UniversityWuxiChina
  3. 3.School of Medical Information EngineeringJining Medical UniversityRizhaoChina
  4. 4.School of ScienceJiangnan UniversityWuxiChina

Personalised recommendations