Change detection (CD) has, in recent years, shown promising results in remote sensing (RS). The development of deep learning CD (DLCD) has, in even more recent years, taken change detection to another level and it has become more widely researched. However, the research depends on publicly available datasets that have been manually annotated for the task of CD. This method is cumbersome and the resulting datasets do not often include all types of change. In this thesis, the generalizability to different areas and different change types of a model trained on a widely used public dataset is analyzed. Also, the thesis investigates how 3D information from Maxar Technologies 3D models can be used to automatically create new more general datasets for CD with both binary or non-binary outputs. The access to large amounts of satellite images together with 3D information enables the creation of more general datasets that can capture more types of change.The thesis concludes that a model trained on the publicly available dataset does not generalize to other areas or other types of change. Models trained on the automatically generated datasets yield relatively good results which indicates that using 3D information to automatically create large datasets is a valid method for CD. Even non-binary approaches show promising results which enable using to gain more practical information on the change of an area. While the thesis presents encouraging results, work can definitely be done to further improve the generalization of the models and improve the dataset generation.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-194386 |
| Date | January 2023 |
| Creators | Wold, Simon, Sandin, Simon |
| Publisher | Linköpings universitet, Institutionen för systemteknik |
| 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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