@techreport{
 title = {Detecting ice layers in radar images with deep learning},
 type = {techreport},
 year = {2018},
 pages = {2-5},
 issue = {April},
 websites = {https://pdfs.semanticscholar.org/e24d/e190e0e01e0e53003fa83daeb4557859f9f6.pdf},
 id = {fb72b8b1-5f54-3eb5-96b9-2e0835f2b601},
 created = {2019-10-01T17:21:01.973Z},
 file_attached = {false},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2020-05-11T14:43:32.071Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {HamidKamangirMaryamRahnemoonfarDuganDobbsJPaden2018},
 private_publication = {false},
 abstract = {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 earth 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 hybrid Deep Convolutional Network to produce the images of surface and bottom ice boundary as outputs. Our network takes advantage of undecimated wavelet transform to provide the highest level of information from radar images, as well as multi-layer 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.},
 bibtype = {techreport},
 author = {Hamid Kamangir, Maryam Rahnemoonfar, Dugan Dobbs, J Paden, Geoffrey Fox}
}

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