利用全极化SAR数据的极化特征获取海冰密集度的算法

doi:10.13474/j.cnki.11-2246.2024.0214

测绘通报 ›› 2024, Vol. 0 ›› Issue (2): 80-84,89.doi: 10.13474/j.cnki.11-2246.2024.0214

利用全极化SAR数据的极化特征获取海冰密集度的算法

陈星哲¹, 谢涛^1,2,3,4, 王明华¹, 张雪红¹, 李建¹, 白淑英¹

1. 南京信息工程大学遥感与测绘工程学院, 江苏南京 210044;
2. 青岛海洋科学与技术国家实验室区域海洋动力学与数值模拟功能实验室, 山东青岛 266237;
3. 自然资源部遥感导航一体化应用工程技术创新中心, 江苏南京 210044;
4. 江苏省协同精密导航定位与智能应用工程研究中心, 江苏南京 210044

收稿日期:2023-07-25 出版日期:2024-02-25 发布日期:2024-03-12
通讯作者: 谢涛。E-mail:xietao@nuist.edu.cn
作者简介:陈星哲(1998—),男,硕士,研究方向为海洋遥感。E-mail:siznzee1998@163.com
基金资助:
国家自然科学基金(42176180);国家重点研发计划(2021YFC2803302)

Algorithm of obtaining sea ice concentration using polarization features from fully polarimetric SAR data

CHEN Xingzhe¹, XIE Tao^1,2,3,4, WANG Minghua¹, ZHANG Xuehong¹, LI Jian¹, BAI Shuying¹

1. School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;
2. Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China;
3. Technology Innovation Center for Integration Applications in Remote Sensing and Navigation, Ministry of Natural Resources, Nanjing 210044, China;
4. Jiangsu Province Engineering Research Center of Collaborative Navigation/Positioning and Smart Application, Nanjing 210044, China

Received:2023-07-25 Online:2024-02-25 Published:2024-03-12

摘要/Abstract

摘要： 本文提出了一种利用全极化SAR数据的极化特征获取海冰密集度算法。首先,对全极化SAR数据进行多视化及滤波等预处理,以获取相干矩阵与协方差矩阵;其次,通过相干矩阵与协方差矩阵获取若干极化特征,对这些极化特征进行相关性与冗余性分析,构建最优特征空间;然后,将最优特征空间作为输入量放入神经网络分类器中,得到海冰分类结果;最后,根据海冰分类结果提取海冰密集度。选用拉布拉多南部海域2景全极化Radarsat-2影像获取海冰密集度,与业务化海冰密集度产品ASI-3125进行对比研究。本文算法结果与ASI-3125海冰密集度分布趋势基本一致,总体上略大于ASI-3125海冰密集度,标准差值分布为3.46%和6.82%,说明利用高分辨率全极化SAR数据在监测边缘区域小尺寸破碎海冰方面具有优势。

关键词: Radarsat-2, 海冰, 海冰密集度, 特征提取, 神经网络

Abstract: This paper proposes an algorithm to obtain sea ice concetration(SIC) from fully polarimetric SAR data based on polarization features. Firstly, multilookprocess and filtering are performed on the fully polarimetric SAR data to obtain the coherence matrix and covariance matrix. Secondly, a number of polarization features are obtained through the coherence matrix and covariance matrix, and the correlation and redundancy analysis of these polarization features is carried out to construct the optimal feature space.Then, put the optimal feature space as input into the neural network classifier to obtain the SIC result. Finally, extract the sea ice concentration according to the SIC result. In this paper, two fully polarimetric Radarsat-2 images in the southern waters of Labrador are used to obtain the SIC. Compared with the commercial the SIC product of ASI-3125, the algorithm results of this paper are basically consistent with the distribution trend of the SIC product of ASI-3125,and generally slightly larger than the SIC product of ASI-3125. The standard deviation distributions are 3.46% and 6.82%, indicating that the use of high-resolution fully polarimetric SAR data has advantages in monitoring small-sized broken sea ice in the marginal area.

Key words: Radarsat-2, sea ice, sea ice concentration, feature extraction, neural network

中图分类号:

P237

陈星哲, 谢涛, 王明华, 张雪红, 李建, 白淑英. 利用全极化SAR数据的极化特征获取海冰密集度的算法[J]. 测绘通报, 2024, 0(2): 80-84,89.

CHEN Xingzhe, XIE Tao, WANG Minghua, ZHANG Xuehong, LI Jian, BAI Shuying. Algorithm of obtaining sea ice concentration using polarization features from fully polarimetric SAR data[J]. Bulletin of Surveying and Mapping, 2024, 0(2): 80-84,89.

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利用全极化SAR数据的极化特征获取海冰密集度的算法

Algorithm of obtaining sea ice concentration using polarization features from fully polarimetric SAR data

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