Supervised multispectral image segmentation with power watersheds

In recent years, graph-based methods have had a significant impact on image segmentation. They are especially noteworthy for supervised segmentation, where the user provides task-specific foreground and background seeds. We adapt the power watershed framework to multispectral and hyperspectral image data and incorporate similarity measures from the field of spectral matching. We also propose a new data-driven graph edge weighting. Our weights are computed by the topological information of a self-organizing map. We show that graph weights based on a simple Lp-norm, as used in other modalities, do not give satisfactory segmentation results for multispectral data, while similarity measures that were specifically designed for this domain perform better. Our new approach is competitive and has an advantage in some of the tested scenarios.