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  • 标题:Support subspaces method for synthetic aperture radar automatic target recognition
  • 作者:Vladimir Fursov ; Denis Zherdev ; Nikolay Kazanskiy
  • 期刊名称:International Journal of Advanced Robotic Systems
  • 印刷版ISSN:1729-8806
  • 电子版ISSN:1729-8814
  • 出版年度:2016
  • 卷号:13
  • 期号:5
  • DOI:10.1177/1729881416664848
  • 语种:English
  • 出版社:SAGE Publications
  • 摘要:This article offers a new object recognition approach that gives high quality using synthetic aperture radar images. The approach includes image preprocessing, clustering and recognition stages. At the image preprocessing stage, we compute the mass centre of object images for better image matching. A conjugation index of a recognition vector is used as a distance function at clustering and recognition stages. We suggest a construction of the so-called support subspaces, which provide high recognition quality with a significant dimension reduction. The results of the experiments demonstrate that the proposed method provides higher recognition quality (97.8%) than such methods as support vector machine (95.9%), deep learning based on multilayer auto-encoder (96.6%) and adaptive boosting (96.1%). The proposed method is stable for objects processed from different angles.
  • 关键词:Conjugation index; digital image processing; recognition; SAR image; SVM
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