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Article

Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction

1
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
2
School of Geosciences and Info-Physics, Central South University, Changsha 410012, China
*
Author to whom correspondence should be addressed.
Academic Editor: John Trinder
Remote Sens. 2021, 13(6), 1049; https://doi.org/10.3390/rs13061049
Received: 8 February 2021 / Revised: 2 March 2021 / Accepted: 8 March 2021 / Published: 10 March 2021
Most of the existing approaches to the extraction of buildings from high-resolution orthoimages consider the problem as semantic segmentation, which extracts a pixel-wise mask for buildings and trains end-to-end with manually labeled building maps. However, as buildings are highly structured, such a strategy suffers several problems, such as blurred boundaries and the adhesion to close objects. To alleviate the above problems, we proposed a new strategy that also considers the contours of the buildings. Both the contours and structures of the buildings are jointly learned in the same network. The contours are learnable because the boundary of the mask labels of buildings implicitly represents the contours of buildings. We utilized the building contour information embedded in the labels to optimize the representation of building boundaries, then combined the contour information with multi-scale semantic features to enhance the robustness to image spatial resolution. The experimental results showed that the proposed method achieved 91.64%, 81.34%, and 74.51% intersection over union (IoU) on the WHU, Aerial, and Massachusetts building datasets, and outperformed the state-of-the-art (SOTA) methods. It significantly improved the accuracy of building boundaries, especially for the edges of adjacent buildings. The code is made publicly available. View Full-Text
Keywords: structural features; building extraction; remote sensing image; semantic segmentation; deep learning structural features; building extraction; remote sensing image; semantic segmentation; deep learning
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MDPI and ACS Style

Liao, C.; Hu, H.; Li, H.; Ge, X.; Chen, M.; Li, C.; Zhu, Q. Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction. Remote Sens. 2021, 13, 1049. https://doi.org/10.3390/rs13061049

AMA Style

Liao C, Hu H, Li H, Ge X, Chen M, Li C, Zhu Q. Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction. Remote Sensing. 2021; 13(6):1049. https://doi.org/10.3390/rs13061049

Chicago/Turabian Style

Liao, Cheng, Han Hu, Haifeng Li, Xuming Ge, Min Chen, Chuangnong Li, and Qing Zhu. 2021. "Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction" Remote Sensing 13, no. 6: 1049. https://doi.org/10.3390/rs13061049

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