Automatic Estimation of Number of Clusters in Hyperspectral Imagery

One of the most challenging problems in automated clustering of hyperspectral data is determining the number of clusters (NOC) either prior to or during the clustering. We propose a statistical method for best estimating the NOC, not only prior to but also independent of the clustering. This method uses both residual analysis (RA) and change point analysis (CPA) to select a number of candidates. Because the NOC and the data intrinsic dimension (ID) interact with one another in a predictable way, ID can provide useful inferential information about the NOC. Indeed, once the ID has been found, the NOC can be inferred on the basis of this information. The performance of the proposed method is evaluated by processing several hyperspectral datasets. Furthermore, a comparison with the results of the partitional approach, using some well-known similarity measures, has demonstrated that our method is more effective in detecting the optimal NOC.