A widely used method for image thresholding was provided by Otsu:
- Otsu 1979 A Threshold Selection Method from Gray-Level Histograms
- Otsu’s method in Wikipedia
- Implementation in Python
However, a global threshold is searched which often is insufficient in cases where land and water are not equally distributed in the image and the histogram doesn’t have a clear minimum. There are several approaches for local thresholding
- A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation as a Test Case
- Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data
- Optimization of threshold ranges for rapid flood inundation mapping by evaluating backscatter profiles of high incidence angle SAR images