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Morphological segmentation/classification of vegetation cover types in remotely sensed images

A methodology based on mathematical morphology operators to classify vegetation cover types in remotely sensed images (ortho and satellite) is proposed in this paper. It consists on the automatic creation of the training sets by integrating the data extracted at higher spatial resolution with the corresponding data at higher spectral resolution, on the geometrical modelling of these sets to create a decision region for each class and on the automatic definition of the elementary units to be classified. The proposed approach is tested and illustrated with remotely sensed images from a region in centre Portugal.