Open Access. Powered by Scholars. Published by Universities.®
- Discipline
Articles 1 - 6 of 6
Full-Text Articles in Engineering
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 …
Survey Of 8 Uav Set-Covering Algorithms For Terrain Photogrammetry, Joshua Hammond, Cory Vernon, Trent Okeson, Benjamin Barrett, Samuel Arce, Valerie Newell, Joseph Janson, Kevin Franke, John Hedengren
Survey Of 8 Uav Set-Covering Algorithms For Terrain Photogrammetry, Joshua Hammond, Cory Vernon, Trent Okeson, Benjamin Barrett, Samuel Arce, Valerie Newell, Joseph Janson, Kevin Franke, John Hedengren
Faculty Publications
Remote sensing with unmanned aerial vehicles (UAVs) facilitates photogrammetry for environmental and infrastructural monitoring. Models are created with less computational cost by reducing the number of photos required. Optimal camera locations for reducing the number of photos needed for structure-from-motion (SfM) are determined through eight mathematical set-covering algorithms as constrained by solve time. The algorithms examined are: traditional greedy, reverse greedy, carousel greedy (CG), linear programming, particle swarm optimization, simulated annealing, genetic, and ant colony optimization. Coverage and solve time are investigated for these algorithms. CG is the best method for choosing optimal camera locations as it balances number of …
A C-Band Scatterometer Simultaneous Wind/Rain Retrieval Method, David G. Long, Congling Nie
A C-Band Scatterometer Simultaneous Wind/Rain Retrieval Method, David G. Long, Congling Nie
Faculty Publications
Using collocated ERS scatterometer (ESCAT), Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), and European Centre for Medium-Range Weather Forecasts (ECMWF) data, the effects of rain on ESCAT wind-only retrieval are evaluated. Additional scattering from rain causes estimated wind speeds to appear higher than expected. Selected directions of the rain-corrupted wind vectors are biased toward along-track directions under conditions of heavy rain, which is regardless of the true wind direction. Rain becomes more significant for data acquired at a high incidence angle. To compensate for rain-induced backscatter, a simultaneous wind/rain retrieval (SWRR) method, which simultaneously retrieves wind velocity and surface …
An Iterative Approach To Multisensor Sea Ice Classification, David G. Long, Mark R. Drinkwater, Quinn P. Remund
An Iterative Approach To Multisensor Sea Ice Classification, David G. Long, Mark R. Drinkwater, Quinn P. Remund
Faculty Publications
Characterizing the variability in sea ice in the polar regions is fundamental to an understanding of global climate and the geophysical processes governing climate changes. Sea ice can be grouped into a number of general classes with different characteristics. Multisensor data from NSCAT, ERS-2, and SSM/I are reconstructed into enhanced resolution imagery for use in ice-type classification. The resulting twelve-dimensional data set is linearly transformed through principal component analysis to reduce data dimensionality and noise levels. An iterative statistical data segmentation algorithm is developed using maximum likelihood (ML) and maximum a posteriori (MAP) techniques. For a given ice type, the …
A Cloud-Removal Algorithm For Ssm/I Data, David G. Long, Douglas L. Daum, Quinn P. Remund
A Cloud-Removal Algorithm For Ssm/I Data, David G. Long, Douglas L. Daum, Quinn P. Remund
Faculty Publications
Microwave radiometers, while traditionally utilized in atmospheric and oceanic studies, can also be used in land surface applications. However, the problem of undesirable atmospheric effects caused by clouds and precipitation must be addressed. In this paper, temporal composite surface brightness images are generated from special sensor microwave/imager (SSM/I) data with the aid of new algorithms to eliminate small-scale distortion caused by clouds or precipitation. Mean, second-highest value, modified maximum average (MMA), and hybrid compositing algorithms are compared. The effectiveness of each algorithm is illustrated through simulation and real data distribution analysis. The results show that the second-highest value algorithm is …
Spatial Resolution Enhancement Of Ssm/I Data, David G. Long, Douglas L. Daum
Spatial Resolution Enhancement Of Ssm/I Data, David G. Long, Douglas L. Daum
Faculty Publications
One of the limitations in using Special Sensor Microwave/Imager (SSM/I) data for land and vegetation studies is the relatively low-spatial resolution. To ameliorate this limitation, resolution-enhancement algorithms can be applied to the data. In this paper, the Backus-Gilbert inversion (BGI) technique and the scatterometer image-reconstruction (SIR) algorithm are investigated as possible methods for creating enhanced resolution images from SSM/I data. The two algorithms are compared via both the simulation and the actual SSM/I data. The algorithms offer similar resolution enhancement, though SIR requires significantly less computation. Sample results over two land regions of South America are presented.