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基于多特征融合词包模型的SAR目标鉴别算法

宋文青 王英华 时荔蕙 刘宏伟 保铮

宋文青, 王英华, 时荔蕙, 刘宏伟, 保铮. 基于多特征融合词包模型的SAR目标鉴别算法[J]. 电子与信息学报, 2017, 39(11): 2705-2715. doi: 10.11999/JEIT170086
引用本文: 宋文青, 王英华, 时荔蕙, 刘宏伟, 保铮. 基于多特征融合词包模型的SAR目标鉴别算法[J]. 电子与信息学报, 2017, 39(11): 2705-2715. doi: 10.11999/JEIT170086
SONG Wenqing, WANG Yinghua, SHI Lihui, LIU Hongwei, BAO Zheng. SAR Target Discrimination Algorithm Based on Bag-of-words Model with Multi-feature Fusion[J]. Journal of Electronics and Information Technology, 2017, 39(11): 2705-2715. doi: 10.11999/JEIT170086
Citation: SONG Wenqing, WANG Yinghua, SHI Lihui, LIU Hongwei, BAO Zheng. SAR Target Discrimination Algorithm Based on Bag-of-words Model with Multi-feature Fusion[J]. Journal of Electronics and Information Technology, 2017, 39(11): 2705-2715. doi: 10.11999/JEIT170086

基于多特征融合词包模型的SAR目标鉴别算法

doi: 10.11999/JEIT170086
基金项目: 

国家自然科学基金(61671354, 61701379),国家杰出青年科学基金(61525105),中央高校基本科研业务费专项资金,陕西省自然科学基础研究计划(2016JQ6048)

SAR Target Discrimination Algorithm Based on Bag-of-words Model with Multi-feature Fusion

Funds: 

The National Natural Science Foundation of China (61671354, 61701379), The National Science Fund for Distinguished Young Scholars of China (61525105), The Fundamental Research Funds for the Central Universities, The Natural Science Basic Research Plan in Shaanxi Province of China (2016JQ6048)

  • 摘要: 针对复杂场景中的SAR目标鉴别问题,该文提出一种基于多特征融合词包(Bag-of-Words, BoW)模型的SAR目标鉴别算法。在BoW模型底层特征提取阶段,算法采用SAR-SIFT特征描述局部区域的形状信息;同时,采用该文基于传统鉴别特征提出的一组新的SAR图像局部特征描述局部区域的对比度信息和纹理信息。对于BoW模型中多个底层特征的融合,算法采用图像层的特征融合方式生成图像的全局鉴别特征,其中各单底层特征BoW模型特征的权系数通过L2范数约束的多核学习方法训练得到。在MiniSAR实测SAR图像数据上的目标鉴别实验表明,与基于传统鉴别特征以及单底层特征BoW模型特征的鉴别算法相比较,该文基于多特征融合BoW模型SAR目标鉴别算法具有更好的鉴别性能。
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    出版历程
    • 收稿日期:  2017-01-23
    • 修回日期:  2017-08-25
    • 刊出日期:  2017-11-19

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