• DocumentCode
    469087
  • Title

    Small-shaped space target recognition based on wavelet decomposition and support vector machine

  • Author

    Zhu, Feng-Yun ; Qin, Shi-Yin

  • Author_Institution
    Beihang Univ., Beijing
  • Volume
    3
  • fYear
    2007
  • fDate
    2-4 Nov. 2007
  • Firstpage
    1397
  • Lastpage
    1402
  • Abstract
    A kind of method for small-shaped space target recognition was proposed in this paper based on feature extraction with wavelet decomposition and formative support vector machine (FSVM) with sequential minimal optimization (SMO) algorithm. Firstly, the significance and characteristics of space target recognition were discussed and a two-stage recognition strategy was designed. And then aiming at the characteristics of small-shaped space target recognition, a new method was implemented based on feature extraction with wavelet decomposition and FSVM with SMO algorithm. Simulation results show the good performance of the algorithm proposed in this paper: the correct rate is more than 97% within 1360 simulation samples of ten classes of small shaped space targets; meanwhile the algorithm is characterized with high speed of near real time in both implementation of training and testing.
  • Keywords
    feature extraction; object recognition; optimisation; support vector machines; wavelet transforms; feature extraction; sequential minimal optimization algorithm; small-shaped space target recognition; support vector machine; wavelet decomposition; Character recognition; Feature extraction; Machine learning; Optimization methods; Pattern recognition; Radar signal processing; Signal processing algorithms; Support vector machines; Target recognition; Wavelet analysis; SMO; SVM; Small-shaped space target; feature extraction; target recognition; wavelet decomposition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-1065-1
  • Electronic_ISBN
    978-1-4244-1066-8
  • Type

    conf

  • DOI
    10.1109/ICWAPR.2007.4421653
  • Filename
    4421653