| 烟花算法在SAR图像属性散射中心参数估计中的应用 |
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| 引用本文： | 周志洪,陈秀真,马进,夏正敏.烟花算法在SAR图像属性散射中心参数估计中的应用[J].红外与激光工程,2022,51(8):20210581-1-20210581-7. |
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| 作者姓名： | 周志洪 陈秀真 马进 夏正敏 |
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| 作者单位： | 1.上海交通大学 网络安全技术研究院，上海 200240 |
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| 基金项目： | 上海市工业强基专项（GYQJ-2018-3-03） |
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| 摘 要： | 针对合成孔径雷达（SAR）属性散射中心估计问题，提出基于烟花算法的方法。首先，在图像域对SAR图像中高能量区域进行分割解耦，获得单个独立散射中心在图像域的表现形式。在此基础上，以属性散射中心参数化模型为基础，构建优化问题，对分离出来的单个散射中心进行最优参数的搜索。在此阶段，引入烟花算法进行参数寻优。该算法具有强大的全局和局部搜索能力，在保证优化精度的条件下避免陷入局部最优，从而保证散射中心参数估计的可靠性。在原始图像中剔除求解后的单个散射中心，对残余图像进行高能量区域分割，序惯估计下一个散射中心的属性参数。最终，获取输入SAR图像上所有散射中心的参数集。实验中，首先基于MSTAR数据集中的SAR图像进行参数估计验证，通过参数估计结果与原始图像的对比以及基于估计参数集对原始图像进行重构，反映了提出算法的有效性。此外，实验还基于估计得到的属性参数进行SAR目标识别算法验证，通过与其他参数估计算法在相同条件下进行识别性能的对比，进一步体现了提出方法在属性散射中心参数估计上的性能优势。
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| 关 键 词： | 合成孔径雷达 属性散射中心 参数估计 烟花算法 |
| 收稿时间： | 2021-12-02 |
Application of firework algorithm into parameter estimation of attributed scattering centers in SAR images |
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| Affiliation: | 1.Institute of Network Security Technology, Shanghai Jiao Tong University, Shanghai 200240, China2.Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai 200240, China |
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| Abstract: | Aiming at the problem of synthetic aperture radar (SAR) attribute scattering center estimation, a method based on the firework algorithm was proposed. First, segmentation and decoupling of high-energy regions in the SAR image are performed in the image domain to obtain the representation of a single independent scattering center in the image domain. Afterwards, based on the parametric model of the attribute scattering center, an optimization problem was constructed to search for the optimal parameters of the separated single scattering center. At this stage, the firework algorithm was introduced to optimize the parameters. The algorithm has strong global and local search capabilities, and avoids falling into the local optimum thus ensuring the optimization accuracy and the reliability of the estimation of the scattering center parameters. The single scattering center after solution was eliminated from the original image, and the residual image was segmented into high-energy regions. And the attribute parameters of the next scattering center were estimated by inertia. Finally, the parameter set of all scattering centers on the input SAR image was obtained. In the implementation, the parameter estimation verification was performed based on the SAR images in the MSTAR dataset. The comparison of the parameter estimation results with the original image and the reconstruction of the original image based on the estimated parameter set reflect the effectiveness of the proposed algorithm. In addition, the experiment also validates the SAR target recognition algorithm based on the estimated attribute parameters. By comparing the recognition performance with other parameter estimation algorithms under the same condition, the performance superiority of the proposed method in the attribute scattering center parameter estimation was further demonstrated. |
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