...
  • 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 >Improved SAR Image Coregistration Using Pixel-Offset Series
【24h】

Improved SAR Image Coregistration Using Pixel-Offset Series

机译：使用像素偏移系列改善SAR图像配准

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) image coregistration is a key procedure before interferometric SAR (InSAR) time-series analysis can be started. However, many geophysical data sets suffer from severe decorrelation problems due to a variety of reasons, making precise coregistration a nontrivial task. Here, we present a new strategy that uses a pixel-offset series of detected subimage patches dominated by point-like targets (PTs) to improve SAR image coregistrations. First, all potentially coherent image pairs are coregistered in a conventional way. In this step, we propose a coregistration quality index for each image to rank its relative “significance” within the data set and to select a reference image for the SAR data set. Then, a pixel-offset series of detected PTs is made from amplitude maps to improve the geometrical mapping functions. Finally, all images are resampled depending on the pixel offsets calculated from the updated geometrical mapping functions. We used images from a rural region near the North Anatolian Fault in eastern Turkey to test the proposed method, and clear coregistration improvements were found based on amplitude stability. This enhanced the fact that the coregistration strategy should therefore lead to improved InSAR time-series analysis results.
机译：在开始干涉式SAR（InSAR）时间序列分析之前，合成孔径雷达（SAR）图像配准是关键过程。但是，由于各种原因，许多地球物理数据集都存在严重的去相关问题，这使得精确的配准成为一项艰巨的任务。在这里，我们提出了一种新策略，该策略使用检测到的由点状目标（PTs）为主的子图像斑块的像素偏移量系列来改善SAR图像核心。首先，以常规方式将所有潜在相干的图像对共配准。在此步骤中，我们为每个图像提出一个配色质量指数，以在数据集中对其相对的“重要性”进行排名，并为SAR数据集选择参考图像。然后，从幅度图制作检测到的PT的像素偏移量系列，以改善几何映射功能。最后，根据从更新的几何映射函数计算出的像素偏移，对所有图像进行重新采样。我们使用了来自土耳其东部北安纳托利亚断层附近的农村地区的图像来测试该方法，并基于振幅稳定性发现了明显的配向改进。这增强了这样一个事实，即整合策略应可导致改善的InSAR时间序列分析结果。

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