Superpixel Classification with the Aid of Neighborhood for Water Mapping in SAR Imagery

Water mapping for satellite imagery has been an active research field for many applications, 1 in particular natural disaster such as flood. Synthetic Aperture Radar (SAR) provides high-resolution 2 imagery without constraint on weather conditions. Single-date SAR approach is less accurate than 3 multi-temporal but can produce results more promptly. This paper proposes novel segmentation 4 schemes that are designed to process both a target superpixel and its surrounding ones for the 5 input for machine learning. We devise MISP-SDT/XGB schemes to generate, annotate, and classify 6 superpixels, and perform land/water segmentation of SAR imagery. These schemes are applied to 7 Sentinel-1 SAR data to examine segmentation performances. Introducing single/mask/neighborhood 8 models in the MISP-SDT scheme and single/neighborhood models in the MISP-XGB, we assess the 9 effects of the contextual information about target and its neighbor superpixels on its segmentation 10 performances. As to polarization, it is shown that the VH mode produces more encouraging results 11 than the VV, which is consistent with previous studies. Also, under our MISP-SDT/XGP schemes 12 the neighborhood models show better performances than FCNN models. Overall, the neighborhood 13 model gives better performances than the single model. Results from attention maps and feature 14 importance scores show that neighbor regions are looked at or used by the algorithms in the neighbor- 15 hood models. Our findings suggest that under our schemes the contextual information has positive 16 effects on land/water segmentation.