...
首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Comparative Study on the Performance of Multiparameter SAR Data for Operational Urban Areas Extraction Using Textural Features
【24h】

Comparative Study on the Performance of Multiparameter SAR Data for Operational Urban Areas Extraction Using Textural Features

机译:基于纹理特征的多参数SAR数据在城市可操作区域提取中性能的比较研究

获取原文
获取原文并翻译 | 示例
           

摘要

The advent of a new generation of synthetic aperture radar (SAR) satellites, such as Advanced SAR/Environmental Satellite (C-band), Phased Array Type L-band Synthetic Aperture Radar/Advanced Land Observing Satellite (L-band), and TerraSAR-X (X-band), offers advanced potentials for the detection of urban tissue. In this letter, we analyze and compare the performance of multiple types of SAR images in terms of band frequency, polarization, incidence angle, and spatial resolution for the purpose of operational urban areas delineation. As a reference for comparison, we use a proven method for extracting textural features based on a Gaussian Markov Random Field (GMRF) model. The results of urban areas delineation are quantitatively analyzed allowing performing intrasensor and intersensors comparisons. Sensitivity of the GMRF model with respect to texture window size and to spatial resolutions of SAR images is also investigated. Intrasensor comparison shows that polarization and incidence angle play a significant role in the potential of the GMRF model for the extraction of urban areas from SAR images. Intersensors comparison evidences the better performances of X-band images, acquired at 1-m spatial resolution, when resampled to resolutions of 5 and 10 m.
机译:新一代合成孔径雷达(SAR)卫星的问世,例如高级SAR /环境卫星(C波段),相控阵型L波段合成孔径雷达/高级陆地观测卫星(L波段)和TerraSAR -X(X波段)为检测城市组织提供了先进的潜力。在这封信中,我们以频带频率,极化,入射角和空间分辨率来分析和比较多种类型的SAR图像的性能,以描绘出可操作的市区区域。作为比较参考,我们使用了一种经过验证的基于高斯马尔可夫随机场(GMRF)模型的纹理特征提取方法。对市区划定的结果进行了定量分析,从而可以进行传感器内和传感器间的比较。还研究了GMRF模型相对于纹理窗口大小和SAR图像空间分辨率的敏感性。传感器内的比较表明,极化和入射角在GMRF模型从SAR图像中提取市区的潜力中起着重要作用。传感器之间的比较证明,当重新采样到5和10 m的分辨率时,以1 m空间分辨率采集的X波段图像具有更好的性能。

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