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Sea Ice Detection from GNSS-R Data Based on Residual Network.

Authors :
Hu, Yuan
Hua, Xifan
Liu, Wei
Wickert, Jens
Source :
Remote Sensing. Sep2023, Vol. 15 Issue 18, p4477. 17p.
Publication Year :
2023

Abstract

Sea ice is an important component of the polar circle and influences atmospheric change. Global navigation satellite system reflectometry (GNSS-R) not only realizes time-continuous and wide-area sea ice detection, but also greatly reduces the cost of sea ice remote sensing research, which has been a hot topic in recent years. To tackle the challenges of noise interference and the reduced accuracy of sea ice detection during the melting period, this paper proposes a sea ice detection method based on a residual neural network (ResNet). ResNet addresses the issue of vanishing gradients in deep neural networks and introduces residual connections, which allows the network to reuse learned features from previous layers. Delay-Doppler maps (DDMs) collected from TechDemoSat-1 (TDS-1) are used as input, and National Oceanic and Atmospheric Administration (NOAA) surface-type data above 60°N are selected as the true values. Based on ResNet, the sea ice detection achieved an accuracy of 98.61%, demonstrating high robustness to noise and strong stability during the sea ice melting period (June to September). In comparison to other sea ice detection algorithms, it stands out with its advantages of high accuracy, stability, and insensitivity to noise. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
15
Issue :
18
Database :
Academic Search Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
172418812
Full Text :
https://doi.org/10.3390/rs15184477