MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images.

We present in this article a new method on unsupervised semantic parsing and structure recognition in peri-urban areas using satellite images. The automatic “building” and “road” detection is based on regions extracted by an unsupervised segmentation method. We propose a novel segmentation algorithm based on a Markov random field model and we give an extensive data analysis for determining relevant features for the classification problem. The novelty of the segmentation algorithm lies on the class-driven vector data quantization and clustering and the estimation of the likelihoods given the resulting clusters. We have evaluated the reachability of a good classification rate using the Random Forest method. We found that, with a limited number of features, among them some new defined in this article, we can obtain good classification performance. Our main contribution lies again on the data analysis and the estimation of likelihoods. Finally, we propose a new method for completing the road network exploiting its connectivity, and the local and global properties of the road network. [ABSTRACT FROM AUTHOR]