Geo-spatial Information Science

, Volume 12, Issue 3, pp 172–181 | Cite as

A fast clonal selection algorithm for feature selection in hyperspectral imagery

  • Yanfei ZhongEmail author
  • Liangpei Zhang


Clonal selection feature selection algorithm (CSFS) based on clonal selection algorithm (CSA), a new computational intelligence approach, has been proposed to perform the task of dimensionality reduction in high-dimensional images, and has better performance than traditional feature selection algorithms with more computational costs. In this paper, a fast clonal selection feature selection algorithm (FCSFS) for hyperspectral imagery is proposed to improve the convergence rate by using Cauchy mutation instead of non-uniform mutation as the primary immune operator. Two experiments are performed to evaluate the performance of the proposed algorithm in comparison with CSFS using hyperspectral remote sensing imagery acquired by the pushbroom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS), respectively. Experimental results demonstrate that the FCSFS converges faster than CSFS, hence providing an effective new option for dimensionality reduction of hyperspectral remote sensing imagery.


hyperspectral feature selection artificial immune systems artificial intelligence 

CLC number



Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Jensen A C, Solberg A S (2007) Fast hyperspectral feature reduction using piecewise constant function approximations [J]. IEEE Geosciences and remote sensing letters, 4(4): 547–551CrossRefGoogle Scholar
  2. [2]
    Hughes G F (1968) On the mean accuracy of statistical pattern recognizers[J]. IEEE Trans. Inf. Theory, IT-14(1): 55–63CrossRefGoogle Scholar
  3. [3]
    Webb A R (2002) Statistical pattern recognition (2nd ed) [M].New York: John Wiley & Sons, Inc.CrossRefGoogle Scholar
  4. [4]
    Chang C-I, Wang S (2006) Constrained band selection for hyperspectral imgery [J]. IEEE Trans. Geosci. Remote Sensing, 44(6): 1 575–1 585Google Scholar
  5. [5]
    Jain A, Zongker D (1997) Feature selection: evaluation, application, and small sample performance [J]. IEEE Trans. Pattern Anal. Machine Intell., 19: 153–158CrossRefGoogle Scholar
  6. [6]
    Pudil P, Novovicova J, Kittler J (1994) Floating search methods in feature selection [J]. Pattern Recognit. Lett., 15:1 119–1 125CrossRefGoogle Scholar
  7. [7]
    Sebastiano B, Lorenzo L (2001) A new search algorithm for feature selection in hyperspectral remote sensing images [J]. IEEE Trans. Geosci. Remote Sensing, 39: 1 360–1 367Google Scholar
  8. [8]
    Raymer M L, Punch W F, Goodman E D, et al. (2000) Dimensionality reduction using genetic algorithm [J]. IEEE Trans. Evolutionary Computation, 4(2): 164–171CrossRefGoogle Scholar
  9. [9]
    Zhang L, Zhong Y, Huang B, et al. (2007) Dimensionality reduction based on clonal selection for hyperspectral imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 45(12): 4 172–4 185CrossRefGoogle Scholar
  10. [10]
    De Castro L N, Von Zuben F J (2000) Clonal selection algorithm with engineering applications [C]. Proc GECCO’s 00, Nevada, USAGoogle Scholar
  11. [11]
    De Castro L N, Von Zuben F J (2002) Learning and optimization using the clonal selection principle [J]. IEEE Transactions on Evolutionary Computation, 6: 239–250CrossRefGoogle Scholar
  12. [12]
    Dasgupta D (1999) Artificial immune systems and their applications [M]. Berlin: Springer-VerlagGoogle Scholar
  13. [13]
    De Castro L N, Timmis J (2002) Artificial immune systems: a new computational intelligence approach [M]. London: Springer-VerlagGoogle Scholar
  14. [14]
    Burnet F M (1959) The clonal selection theory of acquired immunity [M]. Cambridge: Cambridge University PressGoogle Scholar
  15. [15]
    Bruzzone L, Roli F, Serpico S B (1995) An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection [J]. IEEE Trans. Geosci. Remote Sensing, 33: 1 318–1 321CrossRefGoogle Scholar
  16. [16]
    Zhao X, Gao X, Hu Z (2007) Evolutionary programming based on non-uniform mutation [J]. Applied Mathematics and Computation, 192: 1–11CrossRefGoogle Scholar
  17. [17]
    Neubauer A (1997) Adaptive non-uniform mutation for genetic algorithms [M]// Reusch B (Ed.). Computational Intelligence Theory and Applications. Berlin: Springer-VerlagGoogle Scholar
  18. [18]
    Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster [J]. IEEE Transactions on Evolutionary Computation, 3(2): 82–102CrossRefGoogle Scholar
  19. [19]
    Feller W (1971) An introduction to probability theory and its applications [M].New York: John Wiley & SonsGoogle Scholar
  20. [20]
    Chellapilla K (1998) Combining mutation operators in evolutionary programming [J]. IEEE Transactions on Evolutionary Computation, 2(3): 91–96CrossRefGoogle Scholar
  21. [21]
    Hsieh P F, Landgrebe D (1998) Classification of high dimensional data [D]. Indiana, West Lafayette: School Elect. Comput. Eng., Purdue Univ.Google Scholar

Copyright information

© Wuhan University and Springer-Verlag GmbH 2009

Authors and Affiliations

  1. 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanChina

Personalised recommendations