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We propose a fully convolutional neural network (FCNN) model for ice concentration estimation from dual-polarized SAR images. Our network contains 5 convolutional layers. Tested in the Gulf of Saint Lawrence during freeze-up, the proposed model is demonstrated to generate improved ice concentration estimates compared to a CNNs with similar structure.
A two-layer feed forward neural network is used to estimate ice concentration from SAR images directly in this research. SAR image patches are used as input. The CIS (Canadian Ice Service) ice concentration image analyses are used to train the neural network. The experiment shows that the simple neural network can be used to generate a reasonable ice concentration with no preprocessing to the SAR...
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