PhD Advisor: Professor Matthew McCabe

Abstract:
This dissertation explores how hyperspectral CubeSats can provide accessible and scientifically reliable Earth observations despite constraints related to sensor size, power availability, data transmission, calibration, and spatial resolution. Using KAUSTSat, Saudi Arabia’s first hyperspectral CubeSat mission, this research documents mission operations and data acquisition, introduces a deep-learning-based super-resolution method that enhances spatial detail while preserving spectral information, and develops an on-orbit calibration framework using observations from RadCalNet, EnMAP, and EMIT. Together, these contributions establish an end-to-end approach for acquiring, enhancing, calibrating, and validating hyperspectral data from compact satellite platforms, demonstrating their potential for environmental monitoring and future Earth observation applications.  

Bio:
Victor Daniel Angulo Morales is a PhD student in Environmental Science and Engineering. His research focuses on remote sensing and Earth observation, with experience in satellite image processing, hyperspectral imaging, and geographic information systems. During his doctoral studies, he contributed to the processing and scientific analysis of data from the KAUSTSat hyperspectral CubeSat mission. His research interests include small-satellite technologies, hyperspectral remote sensing, unmanned aerial vehicles (UAVs), image enhancement, sensor calibration, and environmental monitoring.