TU4.R13.3
BENCHMARKING DEEP LEARNING AND STATISTICAL TARGET DETECTION METHODS FOR PFM-1 LANDMINE DETECTION IN UAV HYPERSPECTRAL IMAGERY
Sagar Lekhak, Prasanna Reddy Pulakurthi, Ramesh Bhatta, Emmett J. Ientilucci, Rochester Institute of Technology, United States
Session:
TU4.R13: Hyperspectral Image Detection Oral
Track:
Theory and Techniques
Location:
TBD
Presentation Time:
Tue, 11 Aug, 16:45 - 17:00
Presentation
Discussion
Resources
No resources available.
Session TU4.R13
TU4.R13.1: Temporal-Spatial-Spectral Deep Background Reconstruction Model for Anomaly Detection in Hyperspectral Image Sequences
Qiang Ling, Xu He, Kun Li, Yihang Luo, National University of Defense Technology, China
TU4.R13.2: A Tail-Energy Perturbed Black-Box Adversarial Attack for Anti-Detection of Hyperspectral Anomaly
Si-Sheng Young, Chia-Hsiang Lin, National Cheng Kung University, Taiwan
TU4.R13.3: BENCHMARKING DEEP LEARNING AND STATISTICAL TARGET DETECTION METHODS FOR PFM-1 LANDMINE DETECTION IN UAV HYPERSPECTRAL IMAGERY
Sagar Lekhak, Prasanna Reddy Pulakurthi, Ramesh Bhatta, Emmett J. Ientilucci, Rochester Institute of Technology, United States
TU4.R13.4: AN ALGORITHM FOR SUBPIXEL TARGET DETECTION USING A DEEP NEURAL NETWORK WITH ENCODER
Edisanter Lo, Susquehanna University, United States
TU4.R13.5: ON THE IMPACT OF LOGARITHMIC PROCESSING AND NORMALIZATION ON AUTOENCODER-BASED ANOMALY DETECTION IN HYPERSPECTRAL SATELLITE RADIANCE DATA
Alper Koz, Middle East Technical University, Türkiye
Contacts