...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >SAR Imagery Feature Extraction Using 2DPCA-Based Two-Dimensional Neighborhood Virtual Points Discriminant Embedding
【24h】

SAR Imagery Feature Extraction Using 2DPCA-Based Two-Dimensional Neighborhood Virtual Points Discriminant Embedding

机译：基于2DPCA的二维邻域虚拟点判别嵌入的SAR图像特征提取

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Synthetic aperture radar (SAR) is an important microwave sensor that is capable of high-resolution imaging. Extracting valuable features from the SAR target imagery is one of crucial issues in SAR automatic target recognition (ATR). In this paper, we propose a new feature extraction method named 2-D principal-component-analysis-based 2-D neighborhood virtual points discriminant embedding (2DPCA-based 2DNVPDE) for SAR ATR. The SAR imagery is projected into the feature space by 2DPCA and 2DNVPDE in this approach. 2DPCA is able to preserve the global spatial structure of the original imagery, while 2DNVPDE establishes the spatial relationships of the neighborhoods to find the classification information from the neighborhoods of the samples. Hence, our method can extract powerful recognition information and represent the original image in low dimensions. Based on the MSTAR dataset, the experimental results show that the proposed method is able to achieve a higher recognition rate with a lower feature dimension over some existing SAR imagery feature extraction methods. Besides, it indicates that our method has a significant advantage in recognition performance and a lower sensitivity in statistical standpoint.
机译：合成孔径雷达（SAR）是一种重要的微波传感器，能够进行高分辨率成像。从SAR目标图像中提取有价值的特征是SAR自动目标识别（ATR）的关键问题之一。在本文中，我们提出了一种基于2D主成分分析的2维邻域虚拟点判别嵌入（基于2DPCA的2DNVPDE）的SAR ATR特征提取方法。通过这种方法，SAR图像由2DPCA和2DNVPDE投影到特征空间中。 2DPCA能够保留原始图像的全局空间结构，而2DNVPDE则建立邻域的空间关系，以从样本邻域中找到分类信息。因此，我们的方法可以提取强大的识别信息，并以低维表示原始图像。基于MSTAR数据集，实验结果表明，与现有的一些SAR图像特征提取方法相比，该方法能够以较低的特​​征维数实现较高的识别率。此外，这表明我们的方法在识别性能上具有显着的优势，并且在统计角度上具有较低的敏感性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号