Abstract
Image segmentation is a typical task in the field of image processing. There is a great number of image segmentation methods in the literature, but most of these methods are not suitable for multispectral images and they require a priori knowledge. In this work, a hierarchical self-organizing network is proposed for multispectral image segmentation. An advantage of the proposed neural model is due to the hierarchical architecture, which is more flexible in the adaptation process to input data. Experimental results show that the proposed approach is promising for multispectral image processing.
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Palomo, E.J., López-Rubio, E., Domínguez, E., Luque-Baena, R.M. (2013). Hierarchical Self-Organizing Networks for Multispectral Data Visualization. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_48
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DOI: https://doi.org/10.1007/978-3-642-38682-4_48
Publisher Name: Springer, Berlin, Heidelberg
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