Water Detection in SWOT HR Images Based on Multiple Markov Random Fields

One of the main objectives of the surface water and ocean topography (SWOT) mission, scheduled for launch in 2021, is to measure inland water levels using synthetic aperture radar (SAR) interferometry. A key step toward this objective is to precisely detect water areas. In this article, we present a method to detect water in SWOT images. Water is detected based on the relative brightness of the water and nonwater surfaces. Water brightness varies throughout the swath because of system parameters (i.e., the antenna pattern), as well as the phenomenology such as wind speed and surface roughness. To handle the effects of brightness variability, we propose to model the problem with one Markov random field (MRF) on the binary classification map, and two other MRFs to regularize the estimation of the class parameters (i.e., the land and water background power images). Our experiments show that the proposed method is more robust to the expected variations in SWOT images than traditional approaches.