SEA ICE AND OPEN WATER CLASSIFICATION OF SAR IMAGERY USING CNN-BASED TRANSFER LEARNING

Mapping sea ice and open water in the oceans is significant for many applications. Accurate and robust classification methods of sea ice and open water are in demand by ice services. Convolutional neural networks (CNNs) are becoming increasingly popular in many research communities due to availability of large image datasets and high-performance computing systems. As Convolutional networks (ConvNets) have achieved great success on many image classification tasks, we pursue this method for the classification of image patches from synthetic aperture radar (SAR) imagery into ice and water. In this study we use image patches with three dimensions (HH polarization, HV polarization, and incidence angle) in a transfer learning method with CNNs: extracting features of the patches from AlexNet and applying a softmax classifier. Our method achieves an overall classification accuracy of 92.36% based on the held-out test data.