Automatic Segmentation of Water Bodies Using RGB Data: A Physically Based Approach

Abstract

A novel method is proposed to automatically segment water extent using optical data. The key features of this approach are (i) the development of a simple physically based model that utilises only RGB data for water extent segmentation; (ii) the achievement of high accuracy in the results, particularly in the estimation of water surface area and perimeter; (iii) the avoidance of any data training process; (iv) the requirement of minimal computational resources; and (v) the release of an open-source software package that provides both command-line codes and a user-friendly graphical interface, making it accessible for various applications, research, and educational purposes. The physically based model integrates reflectance of the water surface with spectral and quantum interpretation of light. The algorithm was tested on 27 rivers and compared to manually-based delimitation, with a resulting robust segmentation procedure. Quantified errors were RMSE = 11.91 (m2) for surface area, RMSE = 12.25 (m) for perimeter, and RMSE in x: 52 (px), RMSE in y: 93 (px) for centroid location. Processing time was faster for automatic segmentation than manual delimitation, with a time reduction of 40% (case-by-case analysis) and 65% (using all case studies together in one run). Shadows, light spots, and natural and non-natural elements in the field of view may affect the accuracy of results.

Alonso Pizarro

Professor of Hydrology & Hydraulic Engineering

My research interests include Hydrology, Stochastic processes in Hydrology, Fluvial monitoring, UASs, Bridge scour