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Remote Sens. 2018, 10(8), 1195; https://doi.org/10.3390/rs10081195

A Boundary Regulated Network for Accurate Roof Segmentation and Outline Extraction

1
Center for Spatial Information Science, University of Tokyo, Kashiwa 277-8568, Japan
2
Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Received: 16 June 2018 / Revised: 25 July 2018 / Accepted: 26 July 2018 / Published: 30 July 2018
(This article belongs to the Special Issue Remote Sensing based Building Extraction)
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Abstract

The 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 km2 and containing more than 58,000 buildings indicate that our method performs well at both roof segmentation and outline extraction. The proposed BR-Net method significantly outperforms the classic FCN8s model. Compared to the state-of-the-art U-Net model, our BR-Net achieves 6.2% (0.869 vs. 0.818), 10.6% (0.772 vs. 0.698), and 8.7% (0.840 vs. 0.773) improvements in F1 score, Jaccard index, and kappa coefficient, respectively. View Full-Text
Keywords: roof segmentation; outline extraction; convolutional neural network; boundary regulated network; very high resolution imagery roof segmentation; outline extraction; convolutional neural network; boundary regulated network; very high resolution imagery
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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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.

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