Using Combination of Statistical Models and Multilevel Structural Information for Detecting Urban Areas From a Single Gray-Level Image

With the complex building composition and imaging condition, urban areas show versatile characteristics in remote sensing images. In the literature of land-cover analysis, many algorithms utilize the features with structural information to characterize urban areas. Typically, these are more successful on some types of imagery than others, since they usually use only one kind or a few kinds of structural information. On the other hand, since levels of development in neighboring areas are not statistically independent, the multiple features (encoding the multilevel structural information) of each site in urban area depend on that of neighboring sites. In this paper, a new-come discriminative model, i.e., conditional random field (CRF), is introduced to learn the dependencies and fuse the multilevel structural information to obtain the essential detection. To meet the higher needs of some users, we introduce a two-component-based Markov random field model and show how to integrate it tightly with CRF model to refine the results from essential detection. Experiments on a wide range of images show that our algorithms are competitive with recent results in urban area detection