In this paper Bayesian hierarchical model (HDP_IBPs) to classify very high resolution panchromatic as well as multispectral satellite images in an unsupervised way, in which the hierarchical Dirichlet process (HDP) and Indian buffet process (IBP) are combined on multiple scenes. The main contribution of this paper is a novel application framework to solve the problems of traditional probabilistic topic models and achieve the effective unsupervised classification of very high resolution (VHR) panchromatic and multispectral satellite images. The hierarchical structure of our model transmits the spatial information from the original image to the scene layer implicitly and provides useful cues of classification by using clustering technique, clustering is a popular tool for exploratory data analysis, such as K-means clustering technique .Automatic determination of the initialization number of clusters in K-means clustering application is often needed in advance as an input parameter to the algorithm. K-mean clustering algorithm is used to partition and analyze the data which used the required cluster. Initially this number of clusters is taken as starting values. Sometime images which are captures are blur or unclear so they do not return proper return but now with the help of multiple satellite it captures the multiple satellite images and splits them separately.