Abstract: Automatic identification of the building target and precise acquisition of its vector contour has been an urgent task which is at the same time facing huge challenges.In recent years,due to its ability of automatically extracting high-dimensional abstract features with extremely high complexity,convolutional neural network (CNN) have made considerable improvement in this research area,and strongly enhanced the classification accuracy and generalization capability of the state-of-art building detection methods.However,the pooling layers in a classic CNN model actually considerably reduce the spatial resolution of the input image,the building detection results generated from the top layer of CNN often have coarse edges,which poses big challenges for extracting accurate building contour.In order to tackle this problem,an improved fully convolutional network based on U-Net is proposed.First,the structure of U-Net is adopted to detect accurate building edge by using a bottom-up refinement process.Then,by predicting results in both top and bottom layers with the feature pyramid,a twofold constraint strategy is proposed to further improve the detection accuracy.Experiments on aerial imagery datasets covering 30 square kilometers and over 28 000 buildings demonstrate that proposed method performs well for different areas.The accuracy values in the form of average IoU and Kappa are 83.7% and 89.5%,respectively;which are higher than the classic U-Net model,and significantly outperforms the classic full convolutional network model and the AdaBoost model trained with low-level features.

Key words: aerial imagery, building detection, convolutional neural network, U-Net, feature pyramid