Mitigating Camera Artifacts in HYPSO Data for Improved Climate Monitoring
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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
Mitigating Camera Artifacts in HYPSO Data for Improved Climate Monitoring
Project Focus
Current data acquisition methods in the HYPSO satellites suffer from contamination around 800 nm due to something called second-order effects. This is an effect that occurs due to the compact camera design. Marie, a recent graduate, developed a correction method for this issue. While not yet fully implemented to the standard image processing pipeline, it has the potential to expand the usable spectral range towards 900 nm, which would be beneficial for all applications of HYPSO data. This project aims to evaluate and implement known correction method and assess its effectiveness in extending the usable spectral range.
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
Obtain and understand existing correction method documentation for our sensor. Develop a software module to implement the correction method within the existing processing pipeline. Test the implemented correction method on existing spectral data with known contamination. Evaluate the effectiveness of the correction in removing contamination and extending usable range towards 900 nm. Document the findings and recommendations for broader implementation within the processing pipeline.
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 further questions please contact Sivert Bakken
https://www.ntnu.no/ansatte/sivert.bakken