Water Body Extraction Using a Sentinel-1 Scene and Artificial Neural Networks: Case Study - Carpina-PE Dam

Abstract

Surface water is the most important resource and environmental factor for maintaining human survival and ecosystem stability, therefore accurate and timely information on surface water is urgently needed. In this study, an image classification approach using Artificial Neural Networks was proposed for mapping the surface water extent of the Carpina-PE Dam using radar image from the Sentinel-1 satellite, as well as its polarizations (VH and VV) and the generated water indices (SDWI and SWI). All datasets presented limitations in detecting small water bodies, such as narrow rivers, and overestimation in pasture areas, generating commission errors ranging from 16.5% to 28.9% and omission errors ranging from 1.47% and 3.5%, with emphasis on VH and VV polarizations. The overall classification accuracy ranged from 96% to 98% and R² values reached close to 1, where the best performance was seen for SDWI and SWI. The comparative experiments indicated that unitary radar polarizations with water spectral indices were useful for improving the accuracy of extracting water bodies in places with clouds, without significant variations, in addition to providing detailed information, with potential for continuous monitoring.

Keywords:
Deep Learning; Remote Sensing; Synthetic Aperture; Radar Water resources