Single Collect Flood Mapping from VHR X-Band Data Supervised Solely by Ancillary Data

The rapid delineation of water extent in a flood-type event can be very beneficial to disaster relief efforts, and Synthetic Aperture Radar (SAR) is a modality ideally suited for such mapping. However, in a rapid-response scenario, it is desirable to produce such maps independent of historical or external data. To this end, we have propose a scheme to produce flood event maps from a single high-resolution StripMap (SM) imagery acquired from the Capella Space X-band VHR SAR constellation. The learning algorithm is solely trained on publicly available ancillary data, without the use of any human generated labels. The flood-maps are validated quantitatively on non-event scenes against water-occurrence data and qualitatively over the course of a flood-event caused by Hurricane Ida's landfall.