Significant wave height retrieval from Sentinel-1 SAR imagery by convolutional neural network

Xue Sihan; Geng Xupu; Yan Xiao-Hai; Xie Ting; Yu Qiuze

首页> 外文期刊>Journal of oceanography >Significant wave height retrieval from Sentinel-1 SAR imagery by convolutional neural network

【24h】

Significant wave height retrieval from Sentinel-1 SAR imagery by convolutional neural network

机译：由卷积神经网络的Sentinel-1 SAR图像的显着波浪高度检索

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

Significant wave height (SWH) is an important wave parameter that is related to near-shore activities and research on the phenomenon near the air-sea interface. Here, we proposed a new method for retrieving SWH from C-band Sentinel-1 synthetic aperture radar (SAR) interferometric wide mode data based on convolutional neural network (CNN), which can directly establish the empirical relationship between normalized radar cross section and SWH. We collected 1597 Sentinel-1 SAR images matched with in situ buoys and conducted homogeneity tests for each of the matched sub-images, producing similar to 3330 matchups with 2028 of them being VV-polarization. After training, the VV-polarization data extracted by 0.5 threshold for homogeneity test perform better, and the comparison between these results and in situ buoy measurements in validation data indicates a SWH root mean square error of 0.32 m, a 23.58% scatter index and a 0.90 correlation coefficient. And the SWH from CNN-based method is also validated with radar altimeter data and Wavewatch3 data. These results demonstrate that the proposed CNN method is suitable for retrieving SWH from Sentinel-1 SAR imagery with some constraints on the matched dataset.

机译：显着的波浪高度（SWH）是一个重要的波浪参数，与近岸活动有关，对空中界面附近的现象研究。在这里，我们提出了一种基于卷积神经网络（CNN）的C波段哨声-1合成孔径雷达（SAR）干涉宽模式数据检索SWH的新方法，这可以直接建立归一化雷达横截面和SWH之间的经验关系。我们收集了1597个Sentinel-1 SAR图像与原位浮标匹配，并对每个匹配的子图像进行同质性测试，产生类似于3330匹配的，其中2028个具有VV极化。在训练之后，通过0.5阈值提取的VV偏振数据进行同质性测试的更好，并且这些结果与验证数据中的原位浮标测量的比较表示0.32米的SWH螺旋均方误差为0.32米，散射指数为23.58％ 0.90相关系数。来自基于CNN的方法的SWH也用雷达高度计数据和WaveWatch3数据验证。这些结果表明，所提出的CNN方法适用于从Sentinel-1 SAR图像中检索SWH，在匹配的数据集上有一些约束。

著录项

来源
《Journal of oceanography》 |2020年第6期|465-477|共13页
作者
Xue Sihan; Geng Xupu; Yan Xiao-Hai; Xie Ting; Yu Qiuze;
展开▼
作者单位

State Key Lab Marine Environm Sci Xiamen 361005 Peoples R China|Xiamen Univ Coll Ocean & Earth Sci Xiamen 361005 Peoples R China;

State Key Lab Marine Environm Sci Xiamen 361005 Peoples R China|Xiamen Univ Coll Ocean & Earth Sci Xiamen 361005 Peoples R China;

Univ Delaware Coll Earth Ocean & Environm Ctr Remote Sensing Newark DE 19716 USA|Univ Delaware Joint Inst Coastal Res & Management Joint CRM Newark DE 19716 USA|Xiamen Univ Xiamen 361005 Peoples R China;

Univ Delaware Joint Inst Coastal Res & Management Joint CRM Newark DE 19716 USA|Xiamen Univ Xiamen 361005 Peoples R China|Fujian Engn Res Ctr Ocean Remote Sensing Big Data Xiamen 361005 Peoples R China;

Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China;

展开▼
收录信息美国《科学引文索引》(SCI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Synthetic aperture radar; Significant wave height; Convolutional neural network; Sentinel-1;

机译：合成孔径雷达;显着波高;卷积神经网络;哨兵-1;

相似文献

外文文献
中文文献
专利

1. Ocean Wave Parameters Retrieval from Sentinel-1 SAR Imagery [J] . Weizeng Shao, Zheng Zhang, Xiaofeng Li, Remote Sensing . 2016,第9期

机译：从Sentinel-1 SAR影像中检索海浪参数
2. Retrieval of Ocean Wave Heights From Spaceborne SAR in the Arctic Ocean With a Neural Network [J] . Oceanographic Literature Review . 2021,第5期

机译：利用神经网络检索北冰海洋中的海洋波浪高度
3. Oil Rig Recognition Using Convolutional Neural Network on Sentinel-1 SAR Images [J] . Falqueto Leonan E., Sa Jose A. S., Paes Rafael L., IEEE Geoscience and Remote Sensing Letters . 2019,第8期

机译：基于卷积神经网络的Sentinel-1 SAR石油钻机识别
4. Using convolutional neural network (CNN) approach for ship detection in Sentinel-1 SAR imagery [C] . Hashir Tanveer, Timo Balz, Bahaa Mohamdi Asia-Pacific Conference on Synthetic Aperture Radar . 2019

机译：使用卷积神经网络（CNN）方法在Sentinel-1 SAR图像中进行船舶检测
5. Classification of SAR images and retrieval of remotely sensed parameters using neural networks. [D] . Rawat, Vikram Kumar. 1994

机译：SAR图像分类和使用神经网络检索遥感参数。
6. Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks [O] . Hamid Reza Mirsoleimani, Mahmod Reza Sahebi, Nicolas Baghdadi, 2019

机译：基于神经网络的校准IEM和Dubois模型从Sentinel-1 SAR数据中获取裸土表面水分
7. Enhanced Estimation of Significant Wave Height with Dual-Polarization Sentinel-1 SAR Imagery [O] . Fabian Surya Pramudya, Jiayi Pan, Adam Thomas Devlin, 2021

机译：用双极化哨声-1 SAR图像增强了显着波高的估计

Significant wave height retrieval from Sentinel-1 SAR imagery by convolutional neural network

摘要

著录项

相似文献

相关主题

期刊订阅