One thing I always recommend when doing segmentation is first altering the color space; even utilizing a CNN (which should, when trained, essentially perform th

One thing I always recommend when doing segmentation is first altering the color space; even utilizing a CNN (which should, when trained, essentially perform that step). This is especially true of pre-trained models where you don't know if it's been tuned for that. Just in terms of edge detection, you'll see nearly a 10% improvement just from shifting the color space to LAB: https://austingwalters.com/edge-detection-in-computer-vision/ https://austingwalters.com/edge-detection-in-computer-vision... What's even better about the color space conversion, is you can utilize it as a pre-processing step to dramatically reduce the number of edges to search: https://austingwalters.com/chromatags/ https://austingwalters.com/chromatags/ In the case of a blue sea, you can shift to the LAB color space and probably only search the 'A' channel; the channel representing green to red. As the 'B' channel represents yellow to blue. Which is less likely to produce edges in the ocean. This means you only process 1 channel, which dramatically speeds up most algorithms. Just food for thought.