Selection of the Number of Clusters in Remote Sensing Images by Means of Neural Networks

Summary

When processing multidimensional remote sensing data, one of the main problems is the choice for the appropriate number of clusters: despite a great amount of good algorithms for clustering, each of them works properly only when the appropriate number of clusters is selected. As an adaptive version of K-means, the Competitive Learning algorithm (CL) also has a similar crucial problem: different modifications to CL were made with the introduction of frequency sensitive competitive learning (FSCL) and rival penalised competitive learning (RPCL) recently. This last approach introduces an interesting competition mechanism but fails in the presence of real data with multiple clusters of different dimension. We present an improvement of the RPCL algorithm well adapted to work with every kind of real clustering data problems. The basic idea of this new algorithm is to also introduce a competition between the weights in order to allow only one unit to reach the centre of each cluster. The algorithm was tested on multi-band images with different starting weight positions, giving similar results.