A Boundary Regulated Network for Accurate Roof Segmentation and Outline Extraction
AbstractThe automatic extraction of building outlines from aerial imagery for the purposes of navigation and urban planning is a long-standing problem in the field of remote sensing. Currently, most methods utilize variants of fully convolutional networks (FCNs), which have significantly improved model performance for this task. However, pursuing more accurate segmentation results is still critical for additional applications, such as automatic mapping and building change detection. In this study, we propose a boundary regulated network called BR-Net, which utilizes both local and global information, to perform roof segmentation and outline extraction. The BR-Net method consists of a shared backend utilizing a modified U-Net and a multitask framework to generate predictions for segmentation maps and building outlines based on a consistent feature representation from the shared backend. Because of the restriction and regulation of additional boundary information, the proposed model can achieve superior performance compared to existing methods. Experiments on an aerial image dataset covering 32 km
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Wu, G.; Guo, Z.; Shi, X.; Chen, Q.; Xu, Y.; Shibasaki, R.; Shao, X. A Boundary Regulated Network for Accurate Roof Segmentation and Outline Extraction. Remote Sens. 2018, 10, 1195.
Wu G, Guo Z, Shi X, Chen Q, Xu Y, Shibasaki R, Shao X. A Boundary Regulated Network for Accurate Roof Segmentation and Outline Extraction. Remote Sensing. 2018; 10(8):1195.Chicago/Turabian Style
Wu, Guangming; Guo, Zhiling; Shi, Xiaodan; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei. 2018. "A Boundary Regulated Network for Accurate Roof Segmentation and Outline Extraction." Remote Sens. 10, no. 8: 1195.
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