Integrating edge/boundary priors with classification scores for building detection in very high resolution data
Automatic and accurate detection of man-made objects, such as buildings, is one of the main problems that the remote sensing community has been focusing on for the last decades. In this paper, we propose a Conditional Random Field (CRF) formulation which is using edge/boundary localization priors towards accurate building detection. These edge priors have been integrated/fused with the classification scores from a deep learning Convolutional Neural Network (CNN) architecture under a single energy formulation. The validation of the developed methodology had been performed on the recently published SpaceNet dataset. Experimental results and quantitative evaluation, based on different accuracy statistics, indicate the great potential of the proposed approach.