Sea Ice Detection Using Spaceborne GNSS-R Data by VGG16
To address the problems of high noise and low accuracy in the melting period of delay-Doppler map(DDM)data in global navigation satellite system-reflection(GNSS-R)sea ice detection,the VGG16 convolutional neural network model is proposed to be applied to sea ice detection.The multi-level features of DDM are extracted by deep network structure to improve the accuracy and stability of sea ice detection.The experimental results show that the detection accuracy of the proposed VGG16-based sea ice detection method is 98.02％compared with the National Oceanic and Atmospheric Administration(NOAA)surface type data,which effectively improves the accuracy and stability of sea ice detection.
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