Current unsupervised classification methods unable to determine optimum classes and poorly suitable for large and noise-included datasets.A cluster analysis approach based on geometric probability has been put forth

which gradually generates a hierarchical classification scheme in top-down order.The step of further classification for different sub-classes at the same classification level is identical but the executive process is independent for each other.Determining class number

extracting class central position

and searching the boundaries between different classes are performed at each classification level according to the distribution structure of samples in the feature space under the theoretical support of geometric probability.The algorithm of cluster analysis based on geometric probability is contrasted to ERDAS built-in supervised and unsupervised classification algorithms via a case of classifying a TM(thematic mapper) remotely sensed image.It turns out that the one based on geometric probability can obviously improve the classification accuracy.