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Feature selection for SAR images using the hybrid intelligent optimization algorithm

  • role： First author第一作者
  • Affiliation：

    Fundamental Science on Communication Information Transmission and Fusion Technology Laboratory,Hangzhou Dianzi University

ZHANG Qin1,  
  • Affiliation：

    Fundamental Science on Communication Information Transmission and Fusion Technology Laboratory,Hangzhou Dianzi University

GU Yu1,  
  • Affiliation：

    College of life information science & instrument engineering,Hangzhou Dianzi University

XU Ying2,  
  • Affiliation：

    Fundamental Science on Communication Information Transmission and Fusion Technology Laboratory,Hangzhou Dianzi University

LAI Xiaoping1

résumé

To improve the automatic target recognition accuracy of SAR images and real-time performance,this study proposes a feature selection algorithm based on hybrid intelligent optimization for such images. First,a fractal feature is used to enhance an SAR image. An azimuth estimation method is then developed based on the image moment after image segmentation. Subsequently,the features of Zernike moment,Gabor wavelet coefficients,and gray level co-occurrence matrix are extracted from the original and the rectified images to form feature candidates. The genetic algorithm and the binary particle swarm optimization algorithm are combined to select features for SAR images. The effectiveness of the proposed algorithm is verified with the MSTAR database. Results demonstrate that the optimal feature sets can be generalized,thereby improving the target recognition rate and reducing recognition time.

mots-clés

SAR image;feature selection;hybrid intelligent optimization algorithm;fractal feature;Zernike moment

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