A hierarchical unsupervised multispectral model to segment SPOT images for ocean cartography
This paper presents an unsupervised image segmentation method with applications to ocean cartography. By using SPOT satellite data, the aim is to improve the automatic production of bathymetric charts. Indeed, in coastal areas, satellite images provide the radiometry of the electromagnetic waves backscattered by the vegetation, the sea or the sea floor depending on the sea depth in littoral areas. The proposed segmentation method is based on a hierarchical Markovian model defined on a quad-tree, combining multispectral data. One of its interests is to take explicitly into account the correlation between the three spectral channels of the observation. Classification results obtained with synthetic and real images demonstrate the efficiency of the method.