Improving Images for Climate Action
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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
Improving Images for Climate Action
Project Focus
This project focuses on improving the quality of hyperspectral data acquired by HYPSO satellites or other remote sensing systems for enhanced climate monitoring applications. A limitations that this project aim to explore lie in the signal-dependent noise introduced by this type of camera known as imaging spectrometers.
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
Conduct a comprehensive literature review to explore existing noise removal techniques and the state-of-the-art hyperspectral data. Implement and evaluate various algorithms for removing signal-dependent noise from HYPSO data. This may involve established approaches or potentially involve designing a novel algorithm based on recent advancements. Analyze the impact of noise removal on the accuracy and effectiveness for extracting meaningful information from the hyperspectral data. The project aims to demonstrate how improved image restoration techniques can lead to more reliable data for climate monitoring purposes. By enhancing the quality of HYPSO data or other sources of remote sensing data through noise reduction, this project directly contributes Climate Action by providing more accurate information for climate change research and monitoring efforts.
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
Relevant References
https://doi.org/10.3390/rs10030482
https://doi.org/10.1109/MGRS.2017.2762087
https://doi.org/10.1109/WHISPERS.2011.6080866
https://doi.org/10.1109/TGRS.2011.2110657
https://doi.org/10.1109/TGRS.2008.918089
https://doi.org/10.1109/MGRS.2021.3121761