Method for Fine Pattern Recognition of Space Targets Using the Entropy Weight Fuzzy-Rough Nearest Neighbor Algorithm

In space target recognition using spectral analysis technology, there is the problem that the composition or chemical properties of surface materials of the space target are similar. This problem leads to the high similarity of spectral curves and low accuracy of space target recognition. Similar object recognition is important in the study of actual space target observation. In this paper, an entropy weight fuzzy-rough nearest neighbor (EFRNN) algorithm is proposed to enhance the recognition accuracy of similar space targets, which is an improvement of the fuzzy-rough nearest neighbor algorithm. By introducing the feature weight determined using information entropy, the features of all the training samples are considered and quantified. Moreover, the proposed algorithm combined with fuzzy rough set theory can overcome the fuzzy uncertainty caused by overlapping classes and the rough uncertainty caused by insufficient features, to a certain extent. The simulation results show that the proposed algorithm achieves very promising performance compared with existing algorithms. The EFRNN classifier yields an overall classification accuracy of 95.83%. The proposed algorithm is simple and efficient for similar space target recognition. Furthermore, the EFRNN algorithm does not require preset parameters and complex preprocessing.

This is a preview of subscription content, access via your institution.

References

  1. 1.

    Y. Han, H. Sun, J. Feng, and L. Li, Meas. Sci. Technol., 25, No. 7, 075203 (2014).

    ADS  Article  Google Scholar 

  2. 2.

    Q. Li, K. Wu, and Q. Gao, Spectrosc. Spectral Anal., 36, No. 12, 4067–4071 (2016).

    Google Scholar 

  3. 3.

    Q. Deng, H. Lu, H. Tao, M. Hu, and F. Zhao , IEEE Access, 7, 28113–28123 (2019).

    Article  Google Scholar 

  4. 4.

    M. A. A. Cauquya, M. C. Roggemanna, and T. J. Schulz, Proc. SPIE, 5428, 48–57 (2004).

    ADS  Article  Google Scholar 

  5. 5.

    V. P. Pauca, J. Piper, and R. J. Plemmons, Linear Algebra Appl., 416, No. 1, 29–47 (2006).

    MathSciNet  Article  Google Scholar 

  6. 6.

    K. M. Jorgensen, Using Reflectance Spectroscopy to Determine Material Type of Orbital Debris, ProQuest Dissertations, University of Colorado at Boulder, pp. 45–49 (2000).

  7. 7.

    J. Zhang, Chin. J. Opt. Appl. Opt., 2, No. 1, 10–16 (2009) [In Chinese].

    Google Scholar 

  8. 8.

    C. Chang and S. Wang, IEEE T. Geosci. Remote., 44, No. 6, 1575–1585 (2006).

    ADS  Article  Google Scholar 

  9. 9.

    R. Duda and P. Hart, Pattern Classification and Scene Analysis, Wiley, New York, pp. 40–41 (1973).

  10. 10.

    C. Cortes and V. Vapnik, Mach. Learn., 20, 273–297 (1995).

    Google Scholar 

  11. 11.

    M. Sarkar, Fuzzy Set. Syst., 19, No. 158, 2134–2152 (2007).

    Article  Google Scholar 

  12. 12.

    A. Onan, Expert Syst. Appl., 42, No. 20, 6844–6852 (2015).

    Article  Google Scholar 

  13. 13.

    L. Sun and C. Li, In: 2009 WRI Global Congress on Intelligent Systems, May 19–21, Xiamen, China, pp. 311–314 (2009).

  14. 14.

    J. B. Tenenbaum, V. De. Silva, and J. C. Langford, Science, 290, No. 5500, 2319–2323 (2000).

    ADS  Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Qing-bo Li.

Additional information

Published in Zhurnal Prikladnoi Spektroskopii, Vol. 87, No. 6, pp. 886–890, November–December, 2020.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, Qb., Wei, Y. & Li, Wj. Method for Fine Pattern Recognition of Space Targets Using the Entropy Weight Fuzzy-Rough Nearest Neighbor Algorithm. J Appl Spectrosc 87, 1018–1022 (2021). https://doi.org/10.1007/s10812-021-01103-9

Download citation

Keywords

  • fine pattern recognition
  • entropy weight
  • fuzzy-rough set
  • space targets