A novel approach to the automatic classification of remotely 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 together. The initial seed extraction is performed using a simple thresholding strategy applied to NDVI/sub 4-3/ 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.


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    Title :

    Remote sensed images segmentation through shape refinement


    Contributors:
    Gallo, G. (author) / Grasso, G. (author) / Nicotra, S. (author) / Pulvirenti, A. (author)


    Publication date :

    2001-01-01


    Size :

    809278 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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