...
首页> 外文期刊>Canadian Journal of Remote Sensing >Optimal Compact Polarimetric Parameters and Texture Features for Discriminating Sea Ice Types during Winter and Advanced Melt
【24h】

Optimal Compact Polarimetric Parameters and Texture Features for Discriminating Sea Ice Types during Winter and Advanced Melt

机译:最佳紧凑极化参数和质地特征,用于区分冬季和晚期融雪期间的海冰类型

获取原文
获取原文并翻译 | 示例

摘要

C-band synthetic aperture radar (SAR) is widely used for sea ice monitoring and operational activities. The RADARSAT Constellation Mission (RCM), with its anticipated launch in 2018, will provide hybrid compact polarimetric (CP) C-band SAR data offering near-polarimetric capabilities at large image acquisition widths suitable for achieving operational and scientific objectives in the Arctic. Although C-band SAR is effective for sea ice monitoring, it is difficult to implement during advanced melt, when the sea ice cover is melting and covered by melt ponds. Ice type separability during winter (pre-melt) and advanced melt conditions was assessed using Kolmogorov-Smirnov statistical separability analyses and Support Vector Machine supervised classifications of RCM parameters simulated from 2 winter and 2 advanced melt RADARSAT-2 scenes. Through a detailed analysis of the 2 advanced melt scenes, it was found that the steep incidence angle (22.3-24.2 degrees) simulated RCM CP parameters provide improved ice type separability during the advanced melt period compared with shallow incidence angles (39.6-42.2 degrees). With respect to classification, an overall accuracy of 77.06% was found for a scene comprising first-year and multiyear ice types, and a higher overall accuracy of 85.91% was achieved by including gray level co-occurrence matrix parameters in the classification.
机译:C波段合成孔径雷达(SAR)被广泛用于海冰监测和业务活动。 RADARSAT星座任务(RCM)预计将于2018年发射,它将提供混合紧凑型极化(CP)C波段SAR数据,在大图像采集宽度上提供近极化能力,适合在北极地区实现运营和科学目标。尽管C波段SAR对于海冰监测非常有效,但在高级融化过程中,当海冰覆盖层融化并被融化池覆盖时,很难实施。使用Kolmogorov-Smirnov统计可分离性分析和Support Vector Machine监督的RCM参数分类(从2个冬季和2个高级融化RADARSAT-2场景模拟)评估了冬季(预融化)和高级融化条件下的冰类型可分离性。通过对2个高级融化场景的详细分析,发现与较浅的入射角(39.6-42.2度)相比,模拟的RCM CP参数的陡峭入射角(22.3-24.2度)在改进的融化阶段提供了改进的冰型可分离性。 。在分类方面,发现包含一年级和多年期冰类型的场景的总体准确度为77.06%,并且通过在分类中包括灰度共现矩阵参数,可以达到85.91%的更高总体准确度。

著录项

  • 来源
    《Canadian Journal of Remote Sensing 》 |2018年第4期| 390-411| 共22页
  • 作者单位

    Univ Victoria, Dept Geog, 3800 Finnerty Rd, Victoria, BC V8P 5C2, Canada;

    Univ Victoria, Dept Geog, 3800 Finnerty Rd, Victoria, BC V8P 5C2, Canada;

    Univ Victoria, Dept Geog, 3800 Finnerty Rd, Victoria, BC V8P 5C2, Canada;

    Environm & Climate Change Canada, Climate Res Div, 4905 Dufferin St, Toronto, ON M3H 5T4, Canada;

    C Core, 1 Morrissey Rd, St John, NF A1B 3X5, Canada;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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