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

• Published in: Remote Sensing
• Main Authors: Yuan Hu, Xifan Hua, Wei Liu, Jens Wickert
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: delay-Doppler maps (DDMs); global navigation satellite system-reflectometry (GNSS-R); convolutional neural networks (CNNs); sea ice detection; Science; Q; Sea ice
• Online Access: https://doi.org/10.3390/rs15184477; https://doaj.org/article/1cefbf8c38b94a0d8983afd19835a10c

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.