Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods

Inger Bij de Vaate; Ericka Martin; D. Cornelis SlobbeMarc NaeijeMartin Verlaan

首页> 外文期刊>Marine geodesy >Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods

【24h】

Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods

机译：Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods

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
相关主题

摘要

Abstract In the Arctic Ocean, obtaining water levels from satellite altimetry is hampered by the presence of sea ice. Hence, water level retrieval requires accurate detection of fractures in the sea ice (leads). This paper describes a thorough assessment of various surface type classification methods, including a thresholding method, nine supervised-, and two unsupervised machine learning methods, applied to Sentinel-3 Synthetic Aperture Radar Altimeter data. For the first time, the simultaneously sensed images from the Ocean and Land Color Instrument, onboard Sentinel-3, were used for training and validation of the classifiers. This product allows to identify leads that are at least 300 meters wide. Applied to data from winter months, the supervised Adaptive Boosting, Artificial Neural Network, Na?ve-Bayes, and Linear Discriminant classifiers showed robust results with overall accuracies of up to 92. The unsupervised Kmedoids classifier produced excellent results with accuracies up to 92.74 and is an attractive classifier when ground truth data is limited. All classifiers perform poorly on summer data, rendering surface classifications that are solely based on altimetry data from summer months unsuitable. Finally, the Adaptive Boosting, Artificial Neural Network, and Bootstrap Aggregation classifiers obtain the highest accuracies when the altimetry observations include measurements from the open ocean.

著录项

来源
《Marine geodesy》 |2022年第5期|462-495|共34页
作者
Inger Bij de Vaate; Ericka Martin; D. Cornelis SlobbeMarc NaeijeMartin Verlaan;
展开▼
作者单位

Civil Engineering and Geosciences, Delft University of Technology;

Aerospace Engineering, Delft University of Technology;

Delft Institute of Applied Mathematics, Delft University of Technology||Deltares;

展开▼
收录信息
原文格式 PDF
正文语种英语
中图分类
关键词
Arctic Ocean; classification; lead detection; machine learning; Sentinel-3; synthetic aperture radar;

Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods

摘要

著录项

相关主题

期刊订阅