Hierarchical self-organizing networks for multispectral data visualization

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. © 2013 Springer-Verlag Berlin Heidelberg.