Skip to main content
SpringerLink
Log in

A novel band selection method based on curve area and genetic theory

  • Research Article
  • Published:
Journal of Optics Aims and scope Submit manuscript

Abstract

To solve the problem of low detection efficiency of present hyperspectral band selection methods, a new band selection method based on curve area and genetic theory (CAGT) is proposed in this paper. Primarily, the method uses the area under the Receiver Operating Characteristic (ROC) curve as a band selection criterion to measure the target detection effect of the band; then construct fitness function based on this criterion and use genetic algorithm to optimize the band selection. Finally, the band subset with better target detection result can be obtained. Therefore, both data dimensionality reduction and improvement of target detection result can be realized at the same time. Experimental results on a real world hyperspectral data show the efficiency of the proposed CAGT method to improve the detection performance.

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
Fig. 11

Similar content being viewed by others

References

  1. G.F. Hughes, On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory 14(1), 55–63 (1968)

    Article  Google Scholar 

  2. S. Charles, Selecting band combination from multi- spectra1 data. Photogramm. Eng. Remote. Sens. 51(6), 681–687 (1985)

    Google Scholar 

  3. P.S. Chacvez, G.L. Berlin, L.B. Sowers, Statistical method for selecting Landsat MSS ratios. J. Appl. Photogr. Eng. 1(8), 23–30 (1982)

    Google Scholar 

  4. H. J. Su, P. J. Du, Y. H. Sheng, Study on band selection algorithms of hyperspectral image data. Application Research of Computers, 25(4): 1093–1096 (2008)

  5. F. Liu, J.Y. Gong, A classification method for high spatial resolution remotely sensed image based on multi-feature. Geogr. and Geo-Inf. Sci. 25(3), 19–41 (2009)

    MATH  MathSciNet  Google Scholar 

  6. Y. Li, A new bands selection algorithm for hyperspectral image using hyperspectral derivative on Clifford manifold. Inf. Technol. J. 11(7), 904–909 (2012)

    Article  ADS  Google Scholar 

  7. M. Diani, N. Acito, M. Greco, G. Corsini, A new band selection strategy for target detction in hyperspectral images. Proc. 12th Int. Conf. Knowl-Based Intell. Inf. Eng. Syst. 3, 424–431 (2008)

  8. R. Li, J. Liu et al., A systematic approach toward detection of seagrass patches from hyperspectral imagery. Mar. Geod. 35, 271–286 (2012)

    Article  Google Scholar 

  9. H. Gholizadeh et al., A decision fusion framework for hyperspectral subpixel target detection. Photogrammetrie • Fernerkundung • Geoinformation 3, 267–280 (2012)

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China under project No.41174093.

Author information

Authors and Affiliations

  1. Xi’an Research Institute of Hi-Tech, Hongqing Town, Xi’an, 710025, People’s Republic of China

    Yiting Wang, Shiqi Huang, Daizhi Liu & Hongxia Wang

Authors
  1. Yiting Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shiqi Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daizhi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongxia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yiting Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Huang, S., Liu, D. et al. A novel band selection method based on curve area and genetic theory. J Opt 43, 193–202 (2014). https://doi.org/10.1007/s12596-014-0199-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12596-014-0199-4

Keywords

Search

Navigation