Automatic Extraction of the Basal Channel Based on Neural Network

Abstract

The basal channel is a morphological feature that widely exists at the bottom of ice shelves which is mainly formed by the incision of warm water at the base of ice shelves. The study of the basal channel can contribute to further understanding the impact of global warming on polar ice shelves. In this manuscript, the depression caused by the basal channel on the surface of an ice shelf is used to confirm and extract the channel's position. The automatic acquisition of the channel is realized by machine learning. To extract the basal channel more efficiently and minimize the subjectivity caused by visual interpretation, a neural network was applied for the first time, which effectively improved the accuracy of basal channel extraction. We took the manual extraction results of basal channels as the training set input from the model. The improved U-NET network model was used to classify the 79NG ice shelf area, and the extraction accuracy was 73.58%. Then, the trained model was extended to the Peterman ice shelf and Ryder ice shelf located in Greenland. The extraction accuracies were 75.54% and 72.70%, respectively.