Date of Award
8-28-2020
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Systems Engineering
First Advisor
Ibrahim Nisanci
Abstract
Atmospheric studies have been carried out for decades to forecast weather and climate trends such as tropical storms, etc. Of recent concern is the level of greenhouse gases in the atmosphere, which has a relative influence on the weather and climate trends. Over the years, large weather satellites like the GOES series have been used to monitor these GHGs. However, an in-depth understanding of the movement of these gases requires consistent monitoring, which has given rise to the need for miniaturized satellites. Since the miniaturized satellites are novel, they require a ground-truth like the GOES-16 satellite to validate these observations. However, the remotely sensed imagery from the GOES-16 is hampered by the cloud, which disrupts accurate GHG observations. The Cloud-Net (FCN cloud detection) model is used for the cloud pixel detection in the downloaded GOES-16 imagery and, creates a cloud mask to be used for further analysis of water vapor imagery.
Recommended Citation
Adeegbe, Oluwamuyiwa Adesola, "Image Processing and Classification of Remotely-Sensed Satellite Imagery: An Application Towards Cloud Detection" (2020). Theses and Dissertations. 958.
https://research.ualr.edu/etd/958
