Automatic building extraction methods based on deep learning theory have the technical characteristics of high accuracy and speed,and are of great significance in industrial applications, such as urban planning,disaster prevention and mitigation. This paper introduces the deep learning modules and the traditional remote sensing validation section in the proposed building extraction method in high-resolution remote sensing imageries, forming an operational deep-learning-theory based building extraction technical system that integrates different backbone modules, UNet + + networks,and remote sensing authenticity verification modules. The basic network is transformed through the traditional convolutional network model backbones,such as VGG,ResNet, and Inception to improve the model operational efficiency,strengthen the model feature learning capabilities,verify the effectiveness and applicability of the algorithm through authenticity validation. Taking the Massachusetts building dataset published by Mnih as the data source,a comparative analysis was carried out with the traditional non-full convolutional network model and full convolutional network model. The results show that an increasing in the depth and width of the model can substantially improve the building extraction results. The InceptionV3-UNet + + backbone model has the best performance in recall rate,accuracy,CSI,F1 score,Kappa coefficients, and total accuracy,reaching 85. 14%,90. 50%,0. 781 6,0. 877 4,0. 850 4, and 95. 57%,respectively,and its robustness is also verified on the WHU datasets. This method has significantly improved the extraction accuracy and the details of the buildings extracted, especially on complex and irregular buildings, which will facilitate the building extraction applications in real, complex, and large scene of high-resolution remote sensing imageries.
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