Simplified maximum likelihood classification for hyperspectral data in cluster space

In this paper, a simplified maximum likelihood classification method is applied to cluster-space hyperspectral data analysis. A principal components (PC) transformation is firstly used for data de-correlation, followed by cluster-space representation where information classes are associated with spectral clusters automatically. The simplified maximum likelihood classification treats the transformed data independent of the PC features, allowing the second-degree statistics of each cluster to be taken into account with reduced requirement on the number of training samples. Pixel labelling is undertaken by a combined decision based on its membership of belonging to defined clusters and the clusters´ membership of belonging to information classes.