Urban Flood Classification in SAR Images

Abstract

Detecting flooding in Synthetic Aperture Radar (SAR) satellite imagery is crucial for the ability of Google’s flood forecasting team to train predictive models and identify regions at risk of flooding, making it possible to give prior warning to people in soon to be flooded areas.  However, flood detection in urban areas is currently very poor, preventing the extension of these advanced warning systems to large parts of the population. This is a long known challenge in the field of flood detection using remote sensing methods. In this study, we discuss a possible method to overcome this problem. SAR satellites are preferred for flood monitoring due to their effectiveness regardless of weather or environmental conditions. They operate by sending pulse signals to Earth and measuring the reflected backscatter. Smooth surfaces like water typically reflect signals away, appearing darker in SAR images. However, in urban areas, the 'Double Bounce' effect caused by 90-degree surfaces, causes larger backscatter, making water detection challenging. Our methodology involves analyzing abnormally bright pixels in urban areas, attributed to the amplification of the double bounce effect by flooding. We deviate from the traditional thresholding per image approach used in rural settings, instead focusing on the historical brightness levels of each pixel separately to identify significant deviations. We then aggregate the data over large urban areas to infer potential flooding. We optimize and evaluate the model using a train-validation split of a dataset consisting of approximately 70 urban flood events, manually curated from news stories and paired with corresponding SAR images. The evaluation, which compares these images with randomly selected images, yields a precision of 86% and a recall of 62%.  Acquiring high quality ground truth data proved to be one of the big challenges in this project, and we are currently working on other ways to evaluate the model and improve its accuracy. These results demonstrate the potential of using SAR images for urban flood classification by focusing on the unique characteristics of urban areas, such as the double bounce effect. This method shows promise in providing alerts and forecasts for urban regions, a crucial need for disaster management. Further research and more accurate ground truth data could enhance the effectiveness and accuracy of detecting urban floods through SAR images.