首页> 外文学位 >Sea ice monitoring using spaceborne multi-polarization and polarimetric SAR imagery.
【24h】

Sea ice monitoring using spaceborne multi-polarization and polarimetric SAR imagery.

机译:使用星载多极化和极化SAR图像进行海冰监测。

获取原文
获取原文并翻译 | 示例

摘要

The imminent launch of RADARSAT-2, the most advanced of the second-generation spaceborne SARs, has stimulated renewed interest in polarization diversity for sea ice monitoring. The primary objective of this work is to assess the potential value of RADARSAT-2 multi-polarization and polarimetric C-band SAR imagery for classification of sea ice and to develop improved classifiers that account for the characteristics of such imagery.; Our review of the ice information requirements of the Canadian Ice Service reveals the importance of daily revisit for operations. The need to determine the ice edge location, the ice concentration, and stage of development of ice can be addressed by accurate classification of ice types in SAP, imagery.; Our application of target decomposition to AIRSAR airborne polarimetric imagery of sea ice reveals that surface scattering dominates the majority of the scene. Pixel by pixel application of target decomposition methods can be used to distinguish thin ice, first-year ice, and multi-year ice with some success.; When classifying sea ice, we show that a recently proposed K-means clustering algorithm which uses a Wishart classifier can be substantially simplified by initializing it with a seed based solely on backscatter levels. Our analysis of AIRSAR airborne polarimetric imagery of sea ice suggests that classification accuracy obtained using dual-polarization imagery is similar to that of polarimetric imagery and better than that of single-polarization imagery.; Our analysis of simulated RADARSAT-2 polarimetric imagery derived from airborne CV-580 imagery indicates that speckle noise degrades our ability to distinguish between ice types more than the increase in NESZ but can easily be reduced through spatial filtering. In simulated dual-polarization ScanSAR imagery, open water and sea ice can be easily distinguished by using both co- and cross-polarized image in spite of the high NESZ level.; In our analysis of ENVISAT ASAR AP imagery that covers a full ice season, multi-year ice could be distinguished from other types in eight out of ten scenes available. When antenna pattern correction causes a variation of NESZ over the swath, we show that adaptive classification scheme can compensate for such variation.
机译:第二代星载SAR中最先进的RADARSAT-2即将发射,激发了人们对用于海冰监测的极化多样性的新兴趣。这项工作的主要目的是评估RADARSAT-2多极化和极化C波段SAR图像对海冰进行分类的潜在价值,并开发出能够说明此类图像特征的改进分类器。我们对加拿大冰服务局冰信息要求的审查揭示了每日重新访问运营的重要性。确定冰边缘位置,冰浓度和冰发展阶段的需求可以通过在SAP影像中对冰类型进行准确分类来解决。我们将目标分解应用于AIRSAR海冰机载偏振图像中的过程表明,表面散射在大多数场景中占主导地位。目标分解方法的逐像素应用可用于区分薄冰,一年级冰和多年期冰,并取得了一些成功。在对海冰进行分类时,我们表明,仅通过使用基于反向散射级别的种子将其初始化,可以大大简化最近提出的使用Wishart分类器的K均值聚类算法。我们对海冰的AIRSAR机载偏振图像的分析表明,使用双偏振图像获得的分类精度与偏振图像相似,并且比单偏振图像更好。我们对源自机载CV-580图像的模拟RADARSAT-2偏振图像的分析表明,散斑噪声比NESZ的增加要使我们区分冰类型的能力下降更多,但可以通过空间滤波轻松地降低。在模拟的双极化ScanSAR图像中,尽管NESZ值很高,但通过同时使用共极化和交叉极化图像,仍可以轻松地区分开放水和海冰。在我们对覆盖整个冰季的ENVISAT ASAR AP影像的分析中,可以在十个可用场景中的八个场景中将多年冰与其他类型的冰区分开。当天线方向图校正导致整个覆盖范围内的NESZ变化时,我们表明自适应分类方案可以补偿这种变化。

著录项

  • 作者

    Scheuchl, Bernd.;

  • 作者单位

    The University of British Columbia (Canada).;

  • 授予单位 The University of British Columbia (Canada).;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 210 p.
  • 总页数 210
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;
  • 关键词

  • 入库时间 2022-08-17 11:40:49

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