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Full-Text Articles in Physical Sciences and Mathematics
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 …
Learning Set Representations For Lwir In-Scene Atmospheric Compensation, Nicholas M. Westing [*], Kevin C. Gross, Brett J. Borghetti, Jacob A. Martin, Joseph Meola
Learning Set Representations For Lwir In-Scene Atmospheric Compensation, Nicholas M. Westing [*], Kevin C. Gross, Brett J. Borghetti, Jacob A. Martin, Joseph Meola
Faculty Publications
Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed …
Holes: Ionospheric Scintillation, Gps And Imputation, Robert A. Steenburgh
Holes: Ionospheric Scintillation, Gps And Imputation, Robert A. Steenburgh
Theses and Dissertations
Ionospheric scintillation of Global Positioning System (GPS) signals threatens navigation and military operations by degrading performance or making GPS unavailable. Scintillation is particularly active, although not limited to, a belt encircling the earth within ± 20 degrees of the geomagnetic equator. This belt also hosted roughly half of the completed U.S. military operations in the last decade. The authors examined scintillation data from Ascension Island, United Kingdom, and Ancon, Peru, in the Atlantic longitudinal sector as well as data from Parepare, Indonesia, and Marak Parak, Malaysia, in the Pacific longitudinal sector. From these data, they calculate percent probability of occurrence …
Estimation Of Atmospheric Precipitable Water Using The Global Positioning System, David A. Garay
Estimation Of Atmospheric Precipitable Water Using The Global Positioning System, David A. Garay
Theses and Dissertations
This research focuses on using the Global Positioning System (GPS) for atmospheric precipitable water (PW) estimation. Water vapor, measured in terms of PW, plays a crucial role in atmospheric processes and short-term weather forecasting. Traditional methodologies for measuring atmospheric water vapor distributions have known inadequacies, resulting in the motivation to gain good water vapor characterization via GPS. The ability to accurately forecast cloud formation and other weather phenomenon is critical, especially in the case of military operations. Using a network of GPS receivers, it is possible to estimate precipitable water throughout the network region with better accuracy than traditional methods …