New Network Based on Unet++ and Densenet for Building Extraction from High Resolution Satellite Imagery

Extracting building information from remote sensing (RS) images have always played an important role in civil and military. In recent years, many efficient approaches are proposed to detect building in remote sensing images. CNN (Convolutional Neural Networks) has proven to be an effective way of this problem. In this paper, to learn building features better, we propose a convolutional network based on Unet++ and containing dense connections. Our contributions are as follows:(1)Rebuild Unet++ by applying DenseNet as its backbone; (2)Add 1×1 convolution layers that can be introduced as bottleneck layer before DenseBlock to reduce the number of input feature-maps, so as reduce the parameters and improve computational efficiency. The accuracy of building extraction results from the new network which was trained with our training dataset after data augmentation was evaluated with IoU scores. The experimental results show the proposed network has higher IoU scores than Unet++ with fewer parameters.