Supervised Semantic Segmentation of Urban Area Using SAR

Cyclical analyses of dynamic changes in urban areas are critical and necessary for policymakers and societies. Remote sensing data processing methods are currently in place to determine the distribution of built-up and sealed areas on global and continental scales. However, there is a lack of research on distinguishing among urban classes at a larger scale for a city or its district. SAR sensors register features of urban areas that, when further processed, such as textures, can help in automatic recognition. We present a novel dataset for urban classification focusing on density analysis. Machine learning methods, including a selection of artificial neural networks and other classifiers, have been used to distinguish among different classes of built-up areas, as defined according to the Urban Atlas database. This dataset was used to establish benchmarks for classification, conduct verification tests, and evaluate accuracy. The C-band of Sentinel-1 images, for the same study areas as ICEYE X-band images and their texture derivatives, were used to classify variants. Better results were obtained using the CNN-based Unet model. The best overall accuracy was 79% for the X-band and 73% for the C-band datasets. The results indicate that the single-polarization X-band is more suitable for this classification despite the presence of more SAR features in the C-band with dual polarization.