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

• Main Authors: Hu, Yuan, Hua, Xifan, Liu, Wei, Wickert, Jens
• Format: Article in Journal/Newspaper
• Language: English
• Published: Technische Universität Berlin 2023
• Subjects: 500 Naturwissenschaften und Mathematik550 Geowissenschaften, Geologie550 Geowissenschaften; delay-Doppler maps; global navigation satellite system-reflectometry; convolutional neural networks; sea ice detection; DDMs; GNSS-R; CNNs; Sea ice
• Online Access: https://dx.doi.org/10.14279/depositonce-18957; https://depositonce.tu-berlin.de/handle/11303/20159

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 ...