Boundary-aware refined network for automatic building extraction in very high-resolution urban aerial images

Jin, Yuwei; Xu, Wenbo; Zhang, Ce ORCID:; Luo, Xin; Jia, Haitao. 2021 Boundary-aware refined network for automatic building extraction in very high-resolution urban aerial images [in special issue: Advances in object-based image analysis linked with computer vision and machine learning] Remote Sensing, 13 (4), 692. 20, pp.

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Convolutional Neural Networks (CNNs), such as U-Net, have shown competitive performance in the automatic extraction of buildings from Very High-Resolution (VHR) aerial images. However, due to the unstable multi-scale context aggregation, the insufficient combination of multi-level features and the lack of consideration of the semantic boundary, most existing CNNs produce incomplete segmentation for large-scale buildings and result in predictions with huge uncertainty at building boundaries. This paper presents a novel network with a special boundary-aware loss embedded, called the Boundary-Aware Refined Network (BARNet), to address the gap above. The unique properties of the proposed BARNet are the gated-attention refined fusion unit, the denser atrous spatial pyramid pooling module, and the boundary-aware loss. The performance of the BARNet is tested on two popular data sets that include various urban scenes and diverse patterns of buildings. Experimental results demonstrate that the proposed method outperforms several state-of-the-art approaches in both visual interpretation and quantitative evaluations

Item Type: Publication - Article
Digital Object Identifier (DOI):
UKCEH and CEH Sections/Science Areas: Soils and Land Use (Science Area 2017-)
ISSN: 2072-4292
Additional Information. Not used in RCUK Gateway to Research.: Open Access paper - full text available via Official URL link.
Additional Keywords: VHR aerial images, building extraction, convolutional neural network, feature fusion, context aggregation, boundary
NORA Subject Terms: Electronics, Engineering and Technology
Date made live: 24 Feb 2021 11:46 +0 (UTC)

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