A Multi-Scale Technique to Detect Marginal Ice Zones Using Convolutional Neural Networks
Anmol Sharan Nagi, Manpreet Singh Minhas, Linlin Xu, K. Andrea Scott
Abstract
Shipping traffic has grown steadily in the Arctic in recent years. One of the reasons for this increased traffic is the lengthening of open water season, which is accompanied by increases in the area covered by intermediate ice concentrations' or marginal ice zones (MIZs). These regions are difficult to detect using passive microwave data. In this paper, we propose the use of deep learning for automatic detection of MIZs in the RADARSAT-2 satellite images. A synthetic aperture radar (SAR) dataset is manually annotated to train, test and refine the method. Various convolutional neural network (CNN) models are evaluated as fixed feature extractors for the task of classification. To aid the classification accuracy we use a weighted binary cross-entropy loss criterion. Finally, to refine the segmentation process, we used a multi-scale patch technique. The analysis of the results demonstrates that CNN model predictions obtained with multiple sizes of spatial windows is able to detect MIZs in SAR images.