Urban building extraction via visual graphical topic model

This paper addresses the automatic building extraction problem from high-resolution remote sensing images. The buildings in remote sensing images generally represent different shapes (i.e., simple rectangular or complex hybrid shape), it is intractable to extract all the buildings with different shapes once. Therefore, we adopt a hierarchical extraction style with a visual graphical topic model embedded, which includes two stages: the first stage detects the simple rectangular buildings and the second stage extracts the complex hybrid buildings. More specifically, the first stage is mainly responsible for regular buildings detection, and unsupervised visual graphical topic model (i.e., replicated softmax restricted boltzmann machine) and supervised discriminative model learning, and the second stage is mainly in charge of complex buildings extraction using the learned semantic feature mapping and discriminative model. Experimental results show that the second stage can obviously improve the building detection rate with slightly increasing the false alarm rate.