An object-oriented clustering algorithm for VHR panchromatic images using nonparametric latent Dirichlet allocation

In this paper, we present a novel object-oriented semantic clustering algorithm for VHR panchromatic satellite images using a variant of latent Dirichlet allocation model. Firstly, an image collection is implicitly generated by partitioning a large satellite image into densely overlapped sub-images. Then, the Latent Dirichlet Allocation with a hierarchy Dirichlet process is employed to model the image collection. Gibbs sampling is adopted for parameter estimation and image clustering. Specifically, the introduction of Dirichlet process is purposed to extend the LDA to an infinite mixtures model which can estimate the number of components (e.g. clusters in image analysis) automatically. Finally, the effect of the proposed algorithm is analyzed through experiments, and the results of it with the traditional K-means method over a QUICKBIRD image are compared.