Abstract: The utilization of high-resolution remote sensing images to extract urban buildings is one of the current research hotspots, but owing to the different colors, shapes and sizes of buildings, and a wide range of details, the extraction results generally suffer from blurred edges, rounded corners and loss of details. For this reason, this study proposes an E-Unet deep learning network based on cavity convolution. In the structural design, jump connections are introduced to reduce the detail loss of edges and corners; a newly designed convolution module is adopted to expand the perceptual field while reducing the number of parameters; a Dropout module is added to the bottom layer of the network to avoid overfitting; histogram equalization, Gaussian bilateral filtering and inter-band ratio operations are performed on the raw data, which are then combined into a multi-band tensor input network(without conversion to grey-scale images). To validate the network performance and clarify the reasons for the performance improvement, two sets of experiments were designed in this study on the Massachusetts and WHU building datasets. The first set of experiments is a comparison experiment between the E-Unet, Unet and Res-net networks. The results show that E-Unet not only outperforms Unet and ResNet in all accuracy evaluation metrics, but also has high fidelity in the details of the extraction results. The second set of experiments are pre-processing stripping experiments to clarify the performance improvement of the network itself and the pre-processing module. The effectiveness of the pre-processing module and the superiority of the proposed network in this research are demonstrated by the two sets of experiments.

Key words: deep learning, building extraction, atrous convolution, high resolution remote sensing image, E-Unet