Project Focus
Developing Unsupervised Machine Learning Techniques for Hyperspectral Image Analysis of Water Bodies  This project focuses on utilizing unsupervised machine learning, specifically unmixing, to analyze data collected by the NTNU SmallSat Lab's HYPSO satellites. These satellites capture hyperspectral images, providing detailed information beyond the visible spectrum, which is crucial for monitoring water bodies and detecting vital or potentially harmful algae.

Impact
Space technology plays a crucial role in achieving various Sustainable Development Goals (SDGs) set by the UN. The NTNU SmallSat Lab's HYPSO satellites, the first of which was launched in 2022, utilize hyperspectral imagers to capture detailed information beyond the visible. This data allows us to detect and monitor water bodies like oceans, fjords, and lakes, including vital yet potentially harmful algae. 

Tasks and Expected Outcomes
unsupervised machine learning for unmixing 

Who We Are Looking For
We are seeking a highly motivated final year student in Cybernetics, Electronics, or a related field with an interest in image processing and remote sensing applications. Experience with signal processing techniques is not mandatory. The project will be adapted to the student's background and goals. 

How we work
At the NTNU SmallSat Lab we encourage collaboration and try to get our group to help each other. To facilitate this, we have a daily stand-up with the students as well as arrange common lunches and workshops where the students and supervisors can learn from each other.

For more information please contact Joe Garrett. 
https://www.ntnu.no/ansatte/joseph.garrett