Advancing Multisensor Satellite Image Fusion: Techniques, Challenges, and Data Acquisition

Throughout the years of space exploration, the usage of Earth observation satellites has increased tremendously. The usage today extends beyond optical sensors, encompassing radars, infrared, and laser sensors. For this thesis, the usage of optical, synthetic aperture radar, and LiDAR sensors were looked at to see if the fusion of these different sensors could enhance the overall image quality. A crucial aspect of satellite image fusion, regardless of sensor type, is preprocessing to ensure the individual images can be seamlessly merged. Ultimately these preprocessing steps are individual to both the sensors and even different satellites.
The topic of remote sensing and satellite image fusion is extensive and complex. Therefore, this thesis aims to explore various fusion techniques, data sources, and algorithms to contribute to a deeper understanding of the advantages but mostly challenges associated with multisensor satellite image fusion.
A web scraper was developed to collect data from the European Space Agency's Third Party Mission website, a central repository for satellite missions and Earth images.
The scraper made it possible to select different satellites and find the image areas which they had in common. A way to process this data is then presented on how to process the images and finally fuse them. The three fusion algorithms that were used were a simple weighted average, intensity hue saturation, and the pansharpening method. The pansharpening increased both the spatial and spectral resolution whereas the fusion of the optical and synthetic aperture radar gave some mixed results.
There are a lot of things that could be explored in the future, such as utilizing more complex fusion algorithms or using additional satellite sensors. However, the web scraper and the processing flowchart stand as notable achievements of this thesis, simplifying the entire process of multisensor satellite image fusion.