Computer vision for polar science

Students: Jerome Mitchell, Stefan Lee
Faculty: David J. Crandall, Geoffrey C. Fox, John D. Paden

Ground-penetrating radar systems are useful for a variety scientific studies, including monitoring changes to the polar ice sheets that may give clues to climate change. These systems produce vast amounts of radar image data that is typically processed by hand, because the echograms are noisy and difficult to interpret. We are investigating and developing computer vision techniques for analyzing this data, using probabilistic graphical models that can explicitly model the uncertainty and noise in the data. Our techniques are fast (typically milliseconds to seconds per image), and can incorporate a variety of diverse evidence including feedback from a human operator, or measurements from ice cores. We quantitatively evaluate our approaches on large-scale test data, typically including hundreds of echograms and comparing to human-labeled ground truth.

 

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Papers and presentations

BibTeX entries:

@inproceedings{icelayers2021icme,
    title = {Deep Tiered Image Segmentation for Detecting Internal Ice Layers in Radar Imagery},
    booktitle = {IEEE International Conference on Multimedia and Expo (ICME)},
    author = {Yuchen Wang and Mingze Xu and John Paden and Lara Koenig and Geoffrey C. Fox and David J. Crandall},
    year = {2021}
}

@article{icebottom2019jstars,
    journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS)},
    volume = {12},
    number = {9},
    pages = {3272 - 3285},
    author = {Victor Berger and Mingze Xu and Mohanad Al-Ibadi and Shane Chu and David Crandall and John Paden and Geoffrey Fox},
    title = {Automated Ice-Bottom Tracking of 2D and 3D Ice Radar Imagery Using Viterbi and TRW-S},
    year = {2019}
}

@inproceedings{ice2018wacv,
    title = {Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction},
    author = {Mingze Xu and Chenyou Fan and John Paden and Geoffrey Fox and David Crandall},
    year = {2018},
    booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)}
}

@inproceedings{crossover2018radarconf,
    title = {Crossover Analysis and Automated Layer-Tracking Assessment of the Extracted {DEM} of the Basal Topography of the {Canadian Arctic Archipelago} Ice-Cap},
    author = {Mohanad Al-Ibadi and Jordan Sprick and Sravya Athinarapu and Victor Berger and Theresa Stumpf and John Paden and Carl Leuschen and Fernando Rodriguez and Mingze Xu and David Crandall and Geoffrey Fox and David Burgess and Martin Sharp and Luke Copland and Wesley Van Wychen},
    year = {2018},
    booktitle = {IEEE Radar Conference}
}

@inproceedings{icesurface2017icip,
    title = {Automatic estimation of ice bottom surfaces from radar imagery},
    year = {2017},
    booktitle = {IEEE International Conference on Image Processing (ICIP)},
    author = {Mingze Xu and David J. Crandall and Geoffrey C. Fox and John D. Paden}
}

@inproceedings{icelayers2014icip,
    author = {Stefan Lee and Jerome Mitchell and David Crandall and Geoffrey C. Fox},
    title = {Estimating Bedrock and Surface Layer Boundaries and Confidence Intervals in Ice Sheet Radar Imagery using {MCMC}},
    booktitle = {IEEE International Conference on Image Processing (ICIP)},
    year = {2014}
}

@inproceedings{snow2013igarrs,
    title = {Automatic Near Surface Estimation from Snow Radar Imagery},
    author = {Jerome E. Mitchell and David Crandall and Geoffrey Fox and John Paden},
    booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
    year = {2013}
}

@inproceedings{icesheets2012icpr,
    author = {David Crandall and Geoffrey Fox and John Paden},
    title = {Layer-finding in radar echograms using probabilistic graphical models},
    booktitle = {IAPR International Conference on Pattern Recognition (ICPR)},
    year = {2012}
}

Acknowledgements

We gratefully acknowledge the support of the following:

National Science Foundation Lilly Endowment
National Science
Foundation		 NASA IU Data to Insight Center IU Digital Science Center Lilly Endowment
The IU Computer Vision Lab's projects and activities have been funded, in part, by grants and contracts from the Air Force Office of Scientific Research (AFOSR), the Defense Threat Reduction Agency (DTRA), Dzyne Technologies, EgoVid, Inc., ETRI, Facebook, Google, Grant Thornton LLP, IARPA, the Indiana Innovation Institute (IN3), the IU Data to Insight Center, the IU Office of the Vice Provost for Research through an Emerging Areas of Research grant, the IU Social Sciences Research Commons, the Lilly Endowment, NASA, National Science Foundation (IIS-1253549, CNS-1834899, CNS-1408730, BCS-1842817, CNS-1744748, IIS-1257141, IIS-1852294), NVidia, ObjectVideo, Office of Naval Research (ONR), Pixm, Inc., and the U.S. Navy. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government, or any sponsor.