Sparsity-constrained probabilistic latent semantic analysis for land cover classification

Land cover classification can be regarded as topic assignment that the pixels can be classified into different kinds of regions (e.g. road, tree, grass) according to the semantics of topics in topic model. In this paper, we present a novel probabilistic latent semantic analysis (pLSA) model based on sparsity constraint for classifying different kinds of land cover. In contrast with conventional topic model which usually assumes each local feature descriptor is only related to one visual word of the dictionary, our method uses sparse coding to characterize the potential relationship between the descriptor and multiple words. Therefore each descriptor can be represented by a small set of words. More importantly, we further apply sparse coding to mine the correlation of documents (i.e. image) in pLSA model. Consequently, our model can generate the more discriminative latent topics and benefit land cover classification. Experimental results on high-resolution remote sensing images demonstrate the excellent superiority of our method.