Unsupervised Satellite Image Classification Using Markov Field Topic Model

Abstract

Recently, the combination of topic models and random fields has been frequently and successfully applied to image classification due to their complementary effect. However, the number of classes is usually needed to be assigned manually. This letter presents an efficient unsupervised semantic classification method for high-resolution satellite images. We add label cost, which can penalize a solution based on a set of labels that appear in it by optimization of energy, to the random fields of latent topics, and an iterative algorithm is thereby proposed to make the number of classes finally be converged to an appropriate level. Compared with other mentioned classification algorithms, our method not only can obtain accurate semantic segmentation results by larger scale structures but also can automatically assign the number of segments. The experimental results on several scenes have demonstrated its effectiveness and robustness.