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Full-Text Articles in Physics
Physics-Constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios, Nicholas M. Westing
Physics-Constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios, Nicholas M. Westing
Theses and Dissertations
Hyperspectral target detection promises new operational advantages, with increasing instrument spectral resolution and robust material discrimination. Resolving surface materials requires a fast and accurate accounting of atmospheric effects to increase detection accuracy while minimizing false alarms. This dissertation investigates deep learning methods constrained by the processes governing radiative transfer to efficiently perform atmospheric compensation on data collected by long-wave infrared (LWIR) hyperspectral sensors. These compensation methods depend on generative modeling techniques and permutation invariant neural network architectures to predict LWIR spectral radiometric quantities. The compensation algorithms developed in this work were examined from the perspective of target detection performance using …
Monte Carlo And Experimental Analysis Of A Novel Directional Rotating Scatter Mask Gamma Detection System, Julie V. Logan, Darren E. Holland, Larry W. Burggraf, Justin A. Clinton, Buckley E. O'Day Iii
Monte Carlo And Experimental Analysis Of A Novel Directional Rotating Scatter Mask Gamma Detection System, Julie V. Logan, Darren E. Holland, Larry W. Burggraf, Justin A. Clinton, Buckley E. O'Day Iii
Faculty Publications
Excerpt: This work demonstrates successful experimental operation of a prototype system to identify source direction which was modeled using a library of signals simulated using GEANT and a novel algorithm....
Context Aided Tracking With Adaptive Hyperspectral Imagery, Andrew C. Rice
Context Aided Tracking With Adaptive Hyperspectral Imagery, Andrew C. Rice
Theses and Dissertations
A methodology for the context-aided tracking of ground vehicles in remote airborne imagery is developed in which a background model is inferred from hyperspectral imagery. The materials comprising the background of a scene are remotely identified and lead to this model. Two model formation processes are developed: a manual method, and method that exploits an emerging adaptive, multiple-object-spectrometer instrument. A semi-automated background modeling approach is shown to arrive at a reasonable background model with minimal operator intervention. A novel, adaptive, and autonomous approach uses a new type of adaptive hyperspectral sensor, and converges to a 66% correct background model in …