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基于模糊语义的高分辨率SAR图像汽车检测算法

黄勇 刘芳

黄勇, 刘芳. 基于模糊语义的高分辨率SAR图像汽车检测算法[J]. 电子与信息学报, 2017, 39(4): 968-972. doi: 10.11999/JEIT160650
引用本文: 黄勇, 刘芳. 基于模糊语义的高分辨率SAR图像汽车检测算法[J]. 电子与信息学报, 2017, 39(4): 968-972. doi: 10.11999/JEIT160650
HUANG Yong, LIU Fang. Detecting Cars in VHR SAR Images with a Fuzzy Semantic Algorithm[J]. Journal of Electronics and Information Technology, 2017, 39(4): 968-972. doi: 10.11999/JEIT160650
Citation: HUANG Yong, LIU Fang. Detecting Cars in VHR SAR Images with a Fuzzy Semantic Algorithm[J]. Journal of Electronics and Information Technology, 2017, 39(4): 968-972. doi: 10.11999/JEIT160650

基于模糊语义的高分辨率SAR图像汽车检测算法

doi: 10.11999/JEIT160650
基金项目: 

国家973计划项目(2013CB329402),国家自然科学基金(61573267),国家自然科学基金重大研究计划(91438201, 91438103)

Detecting Cars in VHR SAR Images with a Fuzzy Semantic Algorithm

Funds: 

The National 973 Program of China (2013CB329402), The National Natural Science Foundation of China (61573267), The Major Research Plan of the National Natural Science Foundation of China (91438201, 91438103)

  • 摘要: 针对高分辨率SAR图像难于找到精确的背景杂波分布概率模型的问题,该文提出一种不需要背景杂波分布概率模型的高分辨率SAR图像自动检测汽车的新方法。该算法首先搜索场景中包含的亮区域和暗区域,其次采用模糊隶属度函数提取语义特征,筛选可能是汽车强散射区域的亮区域和可能是汽车遮挡区域的暗区域。再根据空间语义关系,对候选汽车强散射区域与候选汽车遮挡区域进行匹配,若匹配成功则计算它们源于同一辆汽车的隶属度。最后阈值选择高隶属度的目标进行合并输出。通过对MiniSAR图像进行汽车检测实验,表明该方法在不需要背景杂波分布概率模型的条件下仍然具有较高的检测率。
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出版历程
  • 收稿日期:  2016-06-21
  • 修回日期:  2016-11-07
  • 刊出日期:  2017-04-19

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