Contextual classification of high-resolution satellite images

We propose a non-homogeneous Conditional Random Field built over an adjacency graph of superpixels for contextual classification of high-resolution satellite images. By introducing the contextual histogram descriptor, our model includes spatially dependent unary and pairwise potentials that capture contextual interactions of the data as well as the labels. This results the non-homogeneity of the fields which improves the accuracy of the classification. Furthermore, our discriminative model performs a multi-cue combination by incorporating efficiently color, texture, edge, curvilinear continuity and familiar configuration cues. As for potentials, both local and global feature functions are learned using joint boosting whereas a likelihood ratio is learned to derive the pairwise edge potential. In this model, the optimal scene interpretation is inferred using a cluster sampling method, the Swendsen-Wang Cut algorithm. Promising results are shown on SPOT-5 satellite images.