Every Variable Everywhere All at Once
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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
- Past Projects
Every Variable Everywhere All at Once
Project Focus
This work will be focused on the so-called curse of dimensionality. You will explore the impact of various methods for dimensionality reductionof data sets with a large set of features and how to aid machine learning algorithms reduce the complexity in these data sets. The suggested methods that you could familiarize yourself with as part of the project thesis includes various approaches for principal component analysis, Partial Least Squares, Neural networks such as autoencoders or transformers, and other algorithms that may interest you.
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
Develop methods for dimensionality reduction of hyperspectral data for improved water quality monitoring using NTNUs satellites.
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