Ni Ruiwen1, Mu Ye1,2,3,4,*, Li Ji1, Zhang Tong1, Luo Tianye1, Feng Ruilong1, Gong He1,2,3,4, Hu Tianli1,2,3,4, Sun Yu1,2,3,4, Guo Ying1,2,3,4, Li Shijun5,6, Thobela Louis Tyasi7
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3263-3274, 2022, DOI:10.32604/cmc.2022.026881
- 16 June 2022
Abstract In order to accurately segment architectural features in high-resolution remote sensing images, a semantic segmentation method based on U-net network multi-task learning is proposed. First, a boundary distance map was generated based on the remote sensing image of the ground truth map of the building. The remote sensing image and its truth map were used as the input in the U-net network, followed by the addition of the building ground prediction layer at the end of the U-net network. Based on the ResNet network, a multi-task network with the boundary distance prediction layer was built. More >
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