WEAKENED WATERSHED ASSEMBLY FOR REMOTE SENSING IMAGE SEGMENTATION AND CHANGE DETECTION
Olivier Debeir, Hussein Atoui, Christophe Simler, Nadine Warzée, Éléonore Wolff · 2009
Marked watershed transform can be seen as a classification in which connected pixels are grouped into components included into the marks catchment basins.The weakened classifier assembly paradigm has shown its ability to give better results than its best member, while generalization and robustness to the noise present in the dataset is increased. We promote in this paper the use of the weakened watershed assembly for remote sensed image segmentation followed by a consensus (vote) of the segmentation results. This approach allows to, but is not restricted to, introduce previously existing borders (e.g. for the map update) in order to constraint the segmentation. We show how the method parameters influence the resulting segmentation and what are the choices the practitioner can make with respect to his problem. A validation of the obtained segmentation is done by comparing with a manual segmentation of the image.