Sharpening Hyperspectral Remote Sensing Data from Miniaturized Imagers
- Project and Master Subjects 2025-2026
-
Past Projects
- Project and Master Subjects 2024-2025
-
Project and Master Subjects 2023-2024
- Multi-satellite data fusion for ocean color remote sensing
- Multimodal ocean color imaging with UAVs
- Hyperspectral super-resolution for ecosystem monitoring in fjords
- Semisupervised algae monitoring from hyperspectral satellites
- Prediction of algal bloom dynamics using ocean simulations
- Sharpening Hyperspectral Remote Sensing Data from Miniaturized Imagers
- MIMO model for water constituents using HYPSO-1 data
- Detection of Large Ships using HYPSO-1 Hyperspectral Remote Sensing Satellite Data
- Unsupervised learning for hyperspectral image segmentation
- Optimal Data Reduction in Miniaturized Hyperspectral Imaging Sensor
- HYPSO-2: Software-defined-radio (SDR) payload integration for HYPSO-2
- Automation of operations for the HYPSO-1 satellite
- Designing a Software-defined-radio (SDR) application experiment for communication between on-ground sensor systems
- HYPSO-3 Mission analysis
- Software Development for CubeSat Payloads for HYPSO-3
- Project and master assignments 2022
MENU
Sharpening Hyperspectral Remote Sensing Data from Miniaturized Imagers
Hyperspectral remote sensing can give more information from a particular scene than conventional imaging, but often at the cost of poorer spatial details. It is possible to improve
the spatial resolution through different image enhancement algorithms. This project will explore that.
Contact
sivert.bakken@ntnu.no