• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Maritime ATR using Classifier Combination and High Resolution Range Profiles
【24h】

Maritime ATR using Classifier Combination and High Resolution Range Profiles

机译：使用分类器组合和高分辨率范围配置文件的海上ATR

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

A maritime automatic target recognition system is developed that performs ship classification using one-dimensional high resolution range profiles. Five physically based features are defined and are extracted from both VV and HH polarizations resulting in a 10-dimensional feature vector. A nonlinear classifier combination approach involving a neural network combiner along with three individual classifiers (Bayes, nearest neighbor, and neural network) is proposed. A decision confidence measure based on the classifier discriminants is developed using a nonparametric estimation approach. The confidence measure enables the system to reject samples that have a low decision confidence. The performance of the proposed neural network based combination is compared with individual classifiers and a number of other combination rules. The results show that this combination can provide high recognition accuracy along with a high probability of declaration. The performance in the presence of samples from not-before-seen classes is also investigated. A new nearest neighbor confidence thresholding approach is developed to aid the neural network combiner in rejecting these samples.
机译：开发了一种海上自动目标识别系统，该系统使用一维高分辨率范围配置文件执行船舶分类。定义了五个基于物理的特征，并从VV和HH极化中提取了这五个特征，从而生成了10维特征向量。提出了一种非线性分类器组合方法，该方法涉及神经网络组合器以及三个单独的分类器（贝叶斯，最近邻和神经网络）。使用非参数估计方法开发了基于分类器判别的决策置信度。置信度度量使系统能够拒绝决策置信度低的样本。将所提出的基于神经网络的组合的性能与单个分类器和许多其他组合规则进行比较。结果表明，这种组合可以提供较高的识别精度以及较高的声明概率。还研究了在未见到的类别的样本存在下的性能。开发了一种新的最近邻置信度阈值化方法，以帮助神经网络组合器拒绝这些样本。

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