Remote Sensed Images Segmentation through Shape Refinement

A novel approach to the automatic classiJcation of remote sensed images is proposed. This approach is based on a three-phase procedure: first pixels which belong to the areas of interest with large likelihood are selected as seeds; second the seeds are refined into connected shapes using two well known image processing techniques; third the results of the shape refinement algorithms are merged togethex The initial seed extraction is performed using a simple thresholding strategy applied to NDVI4-3 index. Subsequently shape refinement through Seeded Region Growing and Watershed Decomposition is applied, finally a merging procedure is applied to build likelihood maps. Experimental results are presented to analyze the correctness and robustness of the method in recognizing vegetation areas around Mount Etna.