Abstract
This work presents a methodology for the land-cover classification of satellite images based on clustering of the Kohonen’s self-organizing map (SOM). The classification task is carried out using a three-stage approach. At the first stage, the SOM is used to quantize and to represent the original patterns of the image in a space of smaller dimension. At the second stage of the method, a filtering process is applied on the SOM prototypes, wherein prototypes associated to input patterns that incorporate more than one land cover class and prototypes that have null activity are excluded in the next stage or simply eliminated of the analysis. At the third and last stage, the SOM prototypes are segmented through a hierarchical clustering method which uses the neighborhood relation of the neurons and incorporates spatial information in its merging criterion. The experimental results show an application example of the proposed methodology on an IKONOS image.
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Gonçalves, M.L., Netto, M.L.A., Costa, J.A.F. (2007). A Three-Stage Approach Based on the Self-organizing Map for Satellite Image Classification. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_70
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DOI: https://doi.org/10.1007/978-3-540-74695-9_70
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