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3D reconstruction of lake surface using camera and lidar sensor fusion
Title
3D reconstruction of lake surface using camera and lidar sensor fusion
Creator
Khan, Shahrukh
Date
2020
Description
Global Navigation Satellite System Reflectometry (GNSS-R) relies upon detecting the GNSS signals reflected off a surface and then analyzing...
Show moreGlobal Navigation Satellite System Reflectometry (GNSS-R) relies upon detecting the GNSS signals reflected off a surface and then analyzing the reflected signal to obtain surface characteristics. GNSS-R has become one of the many additional applications of the readily available GNSS signals, alongside more traditional remote sensing of ionospheric monitoring, beyond the intended GNSS purposes of providing position, navigation, and timing estimation. In previous work, GPS signals reflected off Lake Michigan in Chicago have been collected using a specially designed portable sensor suite. The data collected is then analyzed to differentiate between surface ice and water conditions, as well as obtain other characteristic information such as surface reflectivity. The goal is to provide a way for remote sensing of seasonal ice formation beyond just satellite imagery which can be affected by cloud cover. To confirm the validity of the GNSS-R results there needs to be a separate reference against which to compare. This work demonstrates the sensor fusion between camera and lidar to reconstruct the lake surface, to provide that truth reference for comparison against the results of the GPS reflectometry signal processing. For this setup, the camera provides visual information about the lake surface, while the lidar provides distance information with respect to the sensor suite. Combining the data from the two sensors allows backward projection of the camera image to reconstruct the lake surface and its features. The backward projection relies upon knowledge of the camera's intrinsic properties alongside distance information of the features captured by the camera. Each pixel of the camera image is then transformed to its 3D position relative to the sensor system. This produces a 3D map of the lake surface, as captured by the sensors. The estimated point at which the GPS signal reflects off the surface, the specular point, is calculated by the satellite position at the time of interest and the receiver location. This point is then mapped onto the reconstructed surface to identify the exact location where the signal reflected and compare the surface visually to the results from the signal analysis.Time-varying camera-lidar-specular-point maps of the data campaigns conducted for this project are created for comparison with the GPS signal analysis. Multiple data campaigns were performed during which the Lake Michigan surface had surface ice, water or a mixture of the two. The lake surface is reconstructed for different timestamps, using the appropriate image frame and lidar frame. Combining chronologically, the changes in the lake surface can then be observed along with the movement of the specular point, due to the movement of the GPS satellites. Any satellites passing over a boundary between water and ice on the lake surface are identified and time stamped, to then be compared to the GPS signal analysis results.
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Improvement and Validation of Multiyear Auroral Analysis to Categorize Scintillation Event Layer
Title
Improvement and Validation of Multiyear Auroral Analysis to Categorize Scintillation Event Layer
Creator
English, Breanna R.
Date
2022
Description
Ionospheric irregularities scintillate electromagnetic waves, such as Global Positioning System (GPS) signals, as they pass through the...
Show moreIonospheric irregularities scintillate electromagnetic waves, such as Global Positioning System (GPS) signals, as they pass through the ionosphere, especially in auroral zones. A previous method was developed to determine which layer of the ionosphere these scintillation events occurred in by analyzing optical all sky images (ASI). The results of determining the ionospheric scattering layer using the ratio of 630 nm (red) intensity to 428 nm (blue) intensity were compared to a radar-based method of determining the scintillation layer, and it was found that the results disagreed. In this work, the ASI method is critically analyzed to identify possible errors or sensitivities in the original method that might resolve the discrepancy. This is done by improving and validating the nighttime auroral cloud detection method by comparing to National Oceanic and Atmospheric Administration (NOAA) satellite cloud data. Then a sensitivity analysis is performed on the ASI method to determine which parameters of the method the results are sensitive to. The keogram cloud detection method is improved by automating the selection of the keogram time points that are used to calculate a flat-field gain correction, and by calculating the flat field gain for each year rather than calculatingit once and using it for all years of the study. Keogram cloud detection using the coefficient of variation is verified by comparing the keogram results to true sky conditions based on NOAA cloud mask data, and using detection theory to determine the optimal coefficient of variation threshold. We find that the ideal keogram threshold was 0.37 producing a disagreement rate of 22.4%. The ASI image analysis criteria tested are: the ASI azimuth and elevation mapping files, the magnetic zenith limit, the number of pixels of the ASI that are being analyzed, the duration of the scintillation event that is analyzed, and the red-to-blue ratio threshold. It is found that only changing the red-to-blue ratio threshold has a significant effect on the ASI method, with the red-to-blue ratio that minimizes the number of misattributed layers found to be 1.43.
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