Protecting Water Resources through Machine Learning and Hyperspectral Imaging in Remote Sensing CubeSats
- Project and Master Subjects 2025-2026
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Past Projects
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Project and Master Subjects 2024-2025
- Improving Images for Climate Action
- Every Variable Everywhere All at Once
- Protecting Water Resources through Machine Learning and Hyperspectral Imaging in Remote Sensing CubeSats
- More Effective Earth Observation for Climate Action Through Learned Data Compression in CubeSats
- Mitigating Camera Artifacts in HYPSO Data for Improved Climate Monitoring
- Characterization of High-resolution Spectral Imager
- A Foundational Unmixing Model for the HYPSO satellites
- Hyper/Multispectral image fusion with HYPSO-2
- Game theory applied to energy optimal satellite attitude control
- Mu-analysis for agile satellite attitude control maneuvers
- Randomized optimization applied to super-agile satellite operations
- Modelling super-agile satellite operations for optimization
- Enabling high-accuracy HYPSO image georeferencing by high-accuracy satellite pose estimation through postprocessing of satelitte sensor data
- High-accuracy attitude determination of Earth observation satellites
- Agile Earth Observation Satellite simulation studies
- Multi-angle image analysis and what we can learn about the atmosphere
- GNSS-R: Simulator design of a GNSS-Reflectometry small satellite
- GNSS-R: GNSS jamming and spoofing source localization from a small satellite
- GNSS-R: Maritime Surveillance using GNSS-Reflectometry
- Project and Master Subjects 2023-2024
- Project and master assignments 2022
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Project and Master Subjects 2024-2025
Protecting Water Resources through Machine Learning and Hyperspectral Imaging in Remote Sensing CubeSats
Project Focus
The aim of this project is to develop a model that uses HYPSO data to capture insights for water resources from space. These images are hyperspectral, meaning that they contain a wealth of data invisible to the naked eye. The student will be using machine learning to analyze this data, which can allow us to monitor water quality, track hidden threats, and even predict problems before they happen.
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, launched in 2022 and with a successor planned, 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. The HYPSO satellites also contribute to climate change studies by imaging the Arctic region. Ultimately, HYPSO data aims play a role in achieving Climate Action, Preserving Life Below Water, and ensure access to Clean Water and Sanitation.
Tasks and Expected Outcomes
Collect a robust dataset of hyperspectral imagery from the HYPSO CubeSats and other more conventional satellite infrastructure. A script for planning and scheduling coinciding overpasses with other conventional satellites could be a part of the project thesis. Furthermore, Identification of the optimal subset of variables for accurate model development. A successfully adapted and optimized machine learning model can be considered for the target hardware platform, that is the actual satellite. Validation of the model's performance in an orbital environment will be left for the master thesis.
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 encourage the students to meet us in the lab as well as arrange common lunches and workshops where the students and supervisors can learn from each other.
For further questions please contact Sivert Bakken
https://www.ntnu.no/ansatte/sivert.bakken