...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Texture-Invariant Estimation of Equivalent Number of Looks Based on Trace Moments in Polarimetric Radar Imagery
【24h】

Texture-Invariant Estimation of Equivalent Number of Looks Based on Trace Moments in Polarimetric Radar Imagery

机译：基于迹线矩的极化雷达图像等效视线纹理不变估计

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

This letter introduces a novel estimator of equivalent number of looks (ENL) that can be applied to any distribution of texture model, i.e., an estimator that is texture invariant. The novel estimator is the Development of Trace Moments (DTM), which cancels the textural variation using trace moments. Five forms of the DTM estimator using submatrices are presented and compared with each other. The results show that the full-dimensional matrix form seems to be the best in performance and computational complexity. The experiments were performed using simulated and real data. The comparisons among all the existing methods of ENL estimation in the product model of the clutter, such as K-distribution and G0 distribution, show the performance of the DTM estimator to be the best if there is a sufficient number of samples. The global and local ENL estimations of the real data of San Francisco are analyzed, and the results agree with the simulated case. This shows that the DTM always gives a good result, particularly in the global estimation of ENL. Therefore, it can be concluded that the DTM estimator is robust to any distribution model, with low computational complexity and high accuracy, particularly in wide areas with similar scattering mechanism.
机译：这封信介绍了一种新颖的等值外观估算器（ENL），该估算器可应用于纹理模型的任何分布，即纹理不变的估算器。新颖的估算器是“痕量矩的发展”（DTM），它可以利用痕量矩消除纹理变化。介绍了使用子矩阵的五种形式的DTM估算器，并将其相互比较。结果表明，全维矩阵形式似乎在性能和计算复杂度方面是最好的。实验是使用模拟和真实数据进行的。在杂波产品模型中所有现有ENL估计方法之间的比较（例如K分布和G0分布）表明，如果有足够的样本，DTM估计器的性能将是最佳的。分析了旧金山真实数据的全球和本地ENL估计，其结果与模拟案例一致。这表明DTM总是给出良好的结果，尤其是在ENL的全局估计中。因此，可以得出结论，DTM估计器对于任何分布模型都是鲁棒的，具有较低的计算复杂度和较高的准确性，尤其是在具有类似散射机制的广域中。

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