3D Convolutional Attention Network for Automatically Extracting Ice Layers from Ice Sheet Radar Topological Sequences

Abstract

By analyzing a large number of radar images collected by Radar Depth Sounder instruments, a detailed study of under-ice layer structures can be obtained. However, how to extract the ice layer structures from the ice sheet radar topology sequences quickly and accurately is still a challenge. This paper proposes a method, INNet, which is based on the improved I3D network and combined with the attention module, to automatically extract the ice layer structures at the pixel level. The inception units and non-local attention modules are used in the task of automatically extracting the under-ice structures for the first time. And the experimental results on the dataset of Center of Remote Sensing of Ice Sheets have shown the effectiveness of INNet, whose results show a 12.9% reduction on the measurement of average mean absolute column error compared with the state-of-the-art in deep learning.