Abstract
In this paper, we propose modifications to Kohonen's Self-Organising Feature Map (SOFM) to achieve faster convergence specifically with respect to multispectral images. First, the raw image is pre-processed using data reduction technique to obtain reduced data set and then Condensed Nearest Neighbour (CNN) rule is applied to yield standard subset of samples. The samples in the standard subset are used to find the Best Matching Unit (BMU) and the samples in the reduced data set are used to update BMU and its neighbouring neurons. The SOFM is tested on: synthetic image data set and Harangi 1991, 1992 image data sets. Results are compared with conventional SOFM.
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