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

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Water mapping for satellite imagery has been an active research field for many applications, in particular natural disasters such as floods. Synthetic Aperture Radar (SAR) provides high-resolution imagery without constraints on weather conditions. The single-date SAR approach is less accurate than the multi-temporal approach but can produce results more promptly. This paper proposes novel segmentation schemes that are designed to process both a target superpixel and its surrounding ones for the input for machine learning. Mixture-based Superpixel-Shallow Deit-Ti/XGBoost (MISP-SDT/XGB) schemes are devised to generate, annotate, and classify superpixels, and perform the land/water segmentation of SAR imagery. These schemes are applied to Sentinel-1 SAR data to examine segmentation performances. Single/mask/neighborhood models and single/neighborhood models are introduced in the MISP-SDT scheme and the MISP-XGB scheme, respectively. The effects of the contextual information about the target and its neighbor superpixels are assessed on its segmentation performances. Regarding polarization, it is shown that the VH mode produces more encouraging results than the VV, which is consistent with previous studies. Also, under our MISP-SDT/XGP schemes, the neighborhood models show better performances than FCNN models. Overall, the neighborhood model gives better performances than the single model. Results from attention maps and feature importance scores show that neighbor regions are looked at or used by the algorithms in the neighborhood models. Our findings suggest that under our schemes, the contextual information has positive effects on land/water segmentation.