Enhanced river planform analysis using deep learning and medial axis transform with Sentinel 1A imagery

Abstract

Detecting river centrelines and estimating river water surface widths are valuable for measuring planform geometry. Extracting the river centreline and water surface width estimation from satellite images enables a better understanding of the river network dynamics and can be useful in predicting future changes. This study introduces a novel approach to detect river centrelines and water surface widths that leverages the powerful feature extraction capabilities of DeepLabV3, a state-of-the-art semantic segmentation model and integrates them with the geometric analysis strengths of the Medial Axis Transform (MAT). Our MAT approach identifies the midpoints of the river to detect the centreline. It calculates the distance from the centreline to the edge of the river and estimates the water surface width of the river. The effectiveness of this approach was validated through case studies of the Sipsey, Coosa, Tennessee and Mississippi Rivers. This approach utilizes Sentinel-1A Synthetic Aperture Radar (SAR) imagery, enabling data acquisition independent of the weather conditions. We validated this approach using the National Hydrography Dataset Plus (NHDPlus) and in situ water surface width measurements from the HYDRoacoustic dataset in support of the Surface Water Oceanographic Topography (HYDRoSWOT) and cross-section width from the United States Geological Survey (USGS). The MAT approach accurately extracted centrelines and estimated water surface widths for the straight and meandering river sections. Quantitatively, the fine-tuned DeepLabV3 model achieved a 0.933 F1-score for water mask extraction, while the resulting centreline RMSEs against NHDPlus ranged from 0.55 m (Sipsey River) to 3.10 m (Mississippi River), and water surface width estimations generally varied by 2–15% from in-situ measurements. The accuracy of the method is high for straight and meandering rivers; however, errors, primarily caused by complex river morphology, increased in the Mississippi River because its braided channel system challenged the ability of MAT to define a consistent centreline and water surface width. However, it exhibited reduced accuracy and significant spatial deviations when applied to complex braided sections of the Mississippi River. This approach will significantly advance the field of planform geometry measurement by providing researchers with reliable, scalable and practical methodologies that can be used to develop robust and efficient tools.