With advancements in space technology, remote sensing applications, and computer vision, significant improvements in the data describing our planet are seen today. Researchers want to gather different kinds of data and perform data fusion techniques between them to increase our understanding of the world. Two such data types are Electro-Optical images and Synthetic Aperture Radar images. For data fusion, the images need to be accurately aligned. Researchers have investigated methods for robustly and accurately registering these images for many years. However, recent advancements in imaging systems have made the problem more complex than ever. Currently, the imaging satellites that capture information around the globe have achieved a resolution of less than a meter per pixel. There is an increase in signal complexity for high-resolution SAR images due to how the imaging system operates. Interference between waves gives rise to speckled noise and geometric distortions, making the images very difficult to interpret. This directly affects the image registration accuracy. In this thesis, the complexity of the problem regarding registration between SAR and EO data was described, and methods for registering the images were investigated. The methods were feature- and area-based. The feature-based method used a KAZE filter and SURF descriptor. The method found many key points but few correct correspondences. The area-based methods used FFT and MI, respectively. FFT was deemed best for higher quality images, whereas MI better dealt with the non-linear intensity difference. More complex techniques, such as dense neural networks, were excluded. No method achieved satisfying results on the entire data set, but the area-based methods accomplished complementary results. A conclusion was drawn that the distortions in the SAR images are too significant to register accurately using only CV algorithms. Since the area-based methods achieved good results on images excluding significant distortions, future work should focus on solving the geometrical errors and increasing the registration accuracy
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-186982 |
| Date | January 2022 |
| Creators | Hansson, Niclas |
| Publisher | Linköpings universitet, Datorseende |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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