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Social and Behavioral Sciences Commons™
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- #antcenter (2)
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- Neural network (2)
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- Classification (1)
- Cloud cover (1)
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- GNSS radio occultation (1)
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- Hyperspectral imagery (1)
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Articles 1 - 7 of 7
Full-Text Articles in Social and Behavioral Sciences
Composite Style Pixel And Point Convolution-Based Deep Fusion Neural Network Architecture For The Semantic Segmentation Of Hyperspectral And Lidar Data, Kevin T. Decker, Brett J. Borghetti
Composite Style Pixel And Point Convolution-Based Deep Fusion Neural Network Architecture For The Semantic Segmentation Of Hyperspectral And Lidar Data, Kevin T. Decker, Brett J. Borghetti
Faculty Publications
Multimodal hyperspectral and lidar data sets provide complementary spectral and structural data. Joint processing and exploitation to produce semantically labeled pixel maps through semantic segmentation has proven useful for a variety of decision tasks. In this work, we identify two areas of improvement over previous approaches and present a proof of concept network implementing these improvements. First, rather than using a late fusion style architecture as in prior work, our approach implements a composite style fusion architecture to allow for the simultaneous generation of multimodal features and the learning of fused features during encoding. Second, our approach processes the higher …
Global Gnss-Ro Electron Density In The Lower Ionosphere, Dong L. Wu, Daniel J. Emmons Ii, Nimalan Swarnalingam
Global Gnss-Ro Electron Density In The Lower Ionosphere, Dong L. Wu, Daniel J. Emmons Ii, Nimalan Swarnalingam
Faculty Publications
Lack of instrument sensitivity to low electron density (Ne) concentration makes it difficult to measure sharp Ne vertical gradients (four orders of magnitude over 30 km) in the D/E-region. A robust algorithm is developed to retrieve global D/E-region Ne from the high-rate GNSS radio occultation (RO) data, to improve spatiotemporal coverage using recent SmallSat/CubeSat constellations. The new algorithm removes F-region contributions in the RO excess phase profile by fitting a linear function to the data below the D-region. The new GNSS-RO observations reveal many interesting features in the diurnal, seasonal, solar-cycle, and magnetic-field-dependent variations in the …
A Comparison Of Sporadic-E Occurrence Rates Using Gps Radio Occultation And Ionosonde Measurements, Rodney Carmona, Omar A. Nava, Eugene V. Dao, Daniel J. Emmons
A Comparison Of Sporadic-E Occurrence Rates Using Gps Radio Occultation And Ionosonde Measurements, Rodney Carmona, Omar A. Nava, Eugene V. Dao, Daniel J. Emmons
Faculty Publications
Sporadic-E (Es) occurrence rates from Global Position Satellite radio occultation (GPS-RO) measurements have shown to vary by a factor of five between studies, motivating the need for a comparison with ground-based measurements. In an attempt to find accurate GPS-RO techniques for detecting Es formation, occurrence rates derived using five previously developed GPS-RO techniques are compared to ionosonde measurements over an eight-year period from 2010–2017. GPS-RO measurements within 170 km of a ionosonde site are used to calculate Es occurrence rates and compared to the ground-truth ionosonde measurements. The techniques are compared individually for each ionosonde site …
Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Faculty Publications
Land-cover and land-use classification generates categories of terrestrial features, such as water or trees, which can be used to track how land is used. This work applies classical, ensemble and neural network machine learning algorithms to a multispectral remote sensing dataset containing 405,000 28x28 pixel image patches in 4 electromagnetic frequency bands. For each algorithm, model metrics and prediction execution time were evaluated, resulting in two families of models; fast and precise. The prediction time for an 81,000-patch group of predictions wasmodels, and >5s for the precise models, and there was not a significant change in prediction time when a …
Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco [*], Torrey J. Wagner, Brent T. Langhals
Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco [*], Torrey J. Wagner, Brent T. Langhals
Faculty Publications
Random forest and neural network algorithms are applied to identify cloud cover using 10 of the wavelength bands available in Landsat 8 imagery. The methods classify each pixel into 4 different classes: clear, cloud shadow, light cloud, or cloud. The first method is based on a fully connected neural network with ten input neurons, two hidden layers of 8 and 10 neurons respectively, and a single-neuron output for each class. This type of model is considered with and without L2 regularization applied to the kernel weighting. The final model type is a random forest classifier created from an ensemble of …
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 …
Non-Gnss Smartphone Pedestrian Navigation Using Barometric Elevation And Digital Map-Matching, Daniel Broyles, Kyle J. Kauffman, John F. Raquet, Piotr Smagowski
Non-Gnss Smartphone Pedestrian Navigation Using Barometric Elevation And Digital Map-Matching, Daniel Broyles, Kyle J. Kauffman, John F. Raquet, Piotr Smagowski
Faculty Publications
Pedestrian navigation in outdoor environments where global navigation satellite systems (GNSS) are unavailable is a challenging problem. Existing technologies that have attempted to address this problemoften require external reference signals or specialized hardware, the extra size,weight, power, and cost of which are unsuitable for many applications. This article presents a real-time, self-contained outdoor navigation application that uses only the existing sensors on a smartphone in conjunction with a preloaded digital elevation map. The core algorithm implements a particle filter, which fuses sensor data with a stochastic pedestrian motion model to predict the user’s position. The smartphone’s barometric elevation is then …