Object-oriented clustering of VHR panchromatic images using a nonparametric bayesian model embeded with a latent scene

LDA model has successfully been used to analyzing satellite images. However there are two cucial problems: (1) the number of clusters needs being given in advance, and (2) all documents share a Dirichlet prior. To solve the problems, a novel model include multiple LDAs with variable topics are proposed to cluster satellite images. Each LDA in the model is dedicated to model one kind of natural scene in satellite images. Gibbs sampling method is used to discover natural scenes and learning model parameters. The effect on number of topic estimation is analyzed and then the result of our model is compared with other models. The results indicate that the proposed algorithm outperforms the other comparing models in our experiment.