Deep Hybrid Wavelet Network for Ice Boundary Detection in Radra Imagery

This paper proposes a deep convolutional neural network approach to detect Ice surface and bottom layers from radar imagery. Radar images are capable to penetrate the Ice surface and provide us with valuable information from the underlying layers of ice surface. In recent years, deep hierarchical learning techniques for object detection and segmentation greatly improved the performance of traditional techniques based on hand-crafted feature engineering. We designed a deep convolutional network to produce the images of surface and bottom ice boundary. Our network take advantage of undecimated wavelet transform to provide the higest level of information from radar images, as well as multilayer and multi-scale optimized architecture. In this work, radar images from 2009–2016 NASA Operation IceBridge Mission are used to train and test the network. Our network outperformed the state-of-the art accuracy.