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.
Highlights
In the field of remote sensing, for applications such as urban planning, land use analysis, and automatic updating or generation of maps, automatic extraction of building outlines is a long-standing problem
The main contribution of this paper is that we propose a novel boundary regulated network that improves the performance of the state-of-the-art method (e.g., U-Net) for performing segmentation and outline extraction on very high resolution (VHR) aerial imagery
The best fully convolutional networks (FCNs) variant (FCN8s) and classic U-Net model are adopted as baseline models in our comparisons
Summary
In the field of remote sensing, for applications such as urban planning, land use analysis, and automatic updating or generation of maps, automatic extraction of building outlines is a long-standing problem. Recent years, based on the rapid development of imaging sensors and operating platforms, a dramatic increase in the availability and accessibility of very high resolution (VHR) remote sensing imagery has made this problem increasingly urgent [1]. Resolution, and precision level of extracted data, various methods and algorithms have been proposed for segmenting VHR images [2]. These methods have achieved acceptable precision levels that solve the aforementioned problem to some extent. For additional applications, such as building change detection and automatic mapping, more accurate and robust methods are required
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