Deep hybrid wavelet network for ice boundary detection in radra imagery. Kamangir, H., Rahnemoonfar, M., Dobbs, D., Paden, J., & Fox, G. In International Geoscience and Remote Sensing Symposium (IGARSS), volume 2018-July, pages 3449-3452, 10, 2018. Institute of Electrical and Electronics Engineers Inc.. doi abstract bibtex 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 the 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 convo-lutional network to produce the images of the surface and bottom ice boundary. Our network takes advantage of undecimated wavelet transform to provide the highest 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.
@inproceedings{
title = {Deep hybrid wavelet network for ice boundary detection in radra imagery},
type = {inproceedings},
year = {2018},
keywords = {Deep learning,Holistically nested edge detection,Ice Boundary detection,Radar,Wavelet transform},
pages = {3449-3452},
volume = {2018-July},
month = {10},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
day = {31},
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created = {2019-10-01T17:20:57.298Z},
accessed = {2019-09-03},
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last_modified = {2020-05-11T14:43:32.568Z},
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authored = {true},
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citation_key = {Kamangir2018},
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 ice surface and provide us with valuable information from the underlying layers of the 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 convo-lutional network to produce the images of the surface and bottom ice boundary. Our network takes advantage of undecimated wavelet transform to provide the highest 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.},
bibtype = {inproceedings},
author = {Kamangir, Hamid and Rahnemoonfar, Maryam and Dobbs, Dugan and Paden, John and Fox, Geoffrey},
doi = {10.1109/IGARSS.2018.8518617},
booktitle = {International Geoscience and Remote Sensing Symposium (IGARSS)}
}
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