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  • 标题:Binary Morphological Filtering of Dominant Scattering Area Residues for SAR Target Recognition
  • 作者:Chao Shan ; Bin Huang ; Minggao Li
  • 期刊名称:Computational Intelligence and Neuroscience
  • 印刷版ISSN:1687-5265
  • 电子版ISSN:1687-5273
  • 出版年度:2018
  • 卷号:2018
  • DOI:10.1155/2018/9680465
  • 出版社:Hindawi Publishing Corporation
  • 摘要:A synthetic aperture radar (SAR) target recognition method is proposed in this study based on the dominant scattering area (DSA). DSA is a binary image recording the positions of the dominant scattering centers in the original SAR image. It can reflect the distribution of the scattering centers as well as the preliminary shape of the target, thus providing discriminative information for SAR target recognition. By subtracting the DSA of the test image with those of its corresponding templates from different classes, the DSA residues represent the differences between the test image and various classes. To further enhance the differences, the DSA residues are subject to the binary morphological filtering, i.e., the opening operation. Afterwards, a similarity measure is defined based on the filtered DSA residues after the binary opening operation. Considering the possible variations of the constructed DSA, several different structuring elements are used during the binary morphological filtering. And a score-level fusion is performed afterwards to obtain a robust similarity. By comparing the similarities between the test image and various template classes, the target label is determined to be the one with the maximum similarity. To validate the effectiveness and robustness of the proposed method, experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset and compared with several state-of-the-art SAR target recognition methods.
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