Unsupervised classification of satellite images using K-Harmonic Means Algorithm and Cluster Validity Index.

In this paper, we are presenting a process, which is intended to detect the optimal number of clusters in multispectral remotely sensed images. The proposed process is based on the combination of both the K-Harmonic means and cluster validity index with an angle-based method. The experimental results conducted on both synthetic data sets and real data sets confirm the effectiveness of the proposed methodology. On the other hand, the comparison between the well-known K-means algorithm and the K-Harmonic means shows the superiority of the latter.