Remote Sensing (RS) technology provides valuable information about Earth by enabling an overview of the planet from above, making it a much-needed resource for many applications. Given the abundance of RS data and continued urbanisation, there is a need for efficient approaches to leverage RS data and its unique characteristics for the assessment and management of urban areas. Consequently, employing Deep Learning (DL) for RS applications has attracted much attention over the past few years. In this thesis, novel datasets consisting of satellite RS images over urban areas in Sweden were compiled from Sentinel-2 multispectral, Sentinel-1 Synthetic Aperture Radar (SAR) and Urban Atlas 2018 Land Use/Land Cover (LULC) data. Then, DL was applied for multiband and multiclass semantic image segmentation of LULC. The contributions of complementary spectral, temporal and SAR data and spectral indices to LULC classification performance compared to using only Sentinel-2 data with red, green and blue spectral bands were investigated by implementing DL models based on the fully convolutional network-based architecture, U-Net, and performing data fusion. Promising results were achieved with 25 possible LULC classes. Furthermore, almost all DL models at an overall model level and all DL models at an individual class level for most LULC classes benefited from complementary satellite RS data with varying degrees of classification improvement. Additionally, practical knowledge and insights were gained from evaluating the results and are presented regarding satellite RS data characteristics and semantic segmentation of LULC in urban areas. The obtained results are helpful for practitioners and researchers applying or intending to apply DL for semantic segmentation of LULC in general and specifically in Swedish urban environments.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-195244 |
| Date | January 2023 |
| Creators | Aidantausta, Oskar, Asman, Patrick |
| Publisher | Linköpings universitet, Institutionen för datavetenskap |
| 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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