A MRF-Based Clustering Algorithm for Remote Sensing Images by Using the Latent Dirichlet Allocation Model

MRF (Markov Random Field)-based analysis of remotely sensed imagery provides valuable spatial and structural information that are complementary to pixel-based spectral information in image clustering. In this paper, we present a novel method for semantic clustering of remote sensing images by considering two level of spatial context information in two different ways. First of all, the proposed clustering approach uses a Modified Latent Dirichlet Allocation (MLDA) model to model an image collection, which is implicitly generated by partitioning a large satellite image into densely overlapped sub-images. Then, a folded Gibbs Sampler is employed to estimation the model parameters. At last, image clustering is achieved via the energy minimization technique in the framework of the MRF. Experimental results over a high-resolution satellite image show that (1) unlike traditional pixel-based clustering method, the co-occurrence among pixels is embedded into the clustering algorithm due to the LDA; Consequently, the two geo-object, i.e., shadow and water, in an image could be well separated even although their gray histogram is seriously overlapped; (2) clustering results seems to be more object-oriented based the MRF model.