TH4.R8.3
ROCX: Unsupervised Deep Learning for Multi-Modal Landmine Detection
Xenia Ivashkovych, Hannes De Meulemeester, Charles Hamesse, Rob Haelterman, Royal Military Academy, Belgium
Session:
TH4.R8: ROCX 2025: Data Collections, Ground Experiments, and Early Results II Oral
Track:
Community Contributed Themes
Location:
Embassy
Presentation Time:
Thursday, 13 August, 16:45 - 17:00
Session Co-Chairs:
John Kerekes, Rochester Institute of Technology and Dean Riley, University of Arizona
Presentation
Discussion
Resources
No resources available.
Session TH4.R8
TH4.R8.1: CHROMA: Bridging the Data Gap for Domain-Centric AI in Complex Aquatic Environments
Katarina Doctor, Scott Ramsey, Naval Research Laboratory, United States
TH4.R8.2: The Mélange Experiment at ROCX 2025
Gen Ito, Monica Rios, NGA, United States; Katarina Doctor, Scott Ramsey, NRL, United States; Jesse Figge, Quyen Huynh, NGA, United States
TH4.R8.3: ROCX: Unsupervised Deep Learning for Multi-Modal Landmine Detection
Xenia Ivashkovych, Hannes De Meulemeester, Charles Hamesse, Rob Haelterman, Royal Military Academy, Belgium
TH4.R8.4: MULTIMODAL CO-BORESIGHTED POLARIMETRIC DATASETS FROM ROCX 2025
Bradley Ratliff, University of Dayton, United States
TH4.R8.5: GETBAGS (Ground Effluent Target Bags for Sensor Calibration) Experiment at ROCX
Saketh Ayyalasomayajula, Ian Bortins, Special Technologies Laboratory, United States; John Dibenedetto, Chris Dombrowski, Condor Calibration Services, Inc., United States
Resources
No resources available.