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Remote Sens. 2018, 10(11), 1768;

Building Extraction in Very High Resolution Imagery by Dense-Attention Networks

1,2, 2,3,4,*, 2, 2,3,4,*, 2,4 and 5
School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
Institute of Physical Science and Information Technology, Anhui University, Hefei 230601, China
Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China
Department of Information Engineering, China University of Geosciences, Wuhan 430074, China
Authors to whom correspondence should be addressed.
Received: 6 September 2018 / Revised: 4 November 2018 / Accepted: 6 November 2018 / Published: 8 November 2018
(This article belongs to the Special Issue Remote Sensing based Building Extraction)
PDF [4759 KB, uploaded 8 November 2018]


Building extraction from very high resolution (VHR) imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Compared with the traditional building extraction approaches, deep learning networks have recently shown outstanding performance in this task by using both high-level and low-level feature maps. However, it is difficult to utilize different level features rationally with the present deep learning networks. To tackle this problem, a novel network based on DenseNets and the attention mechanism was proposed, called the dense-attention network (DAN). The DAN contains an encoder part and a decoder part which are separately composed of lightweight DenseNets and a spatial attention fusion module. The proposed encoder–decoder architecture can strengthen feature propagation and effectively bring higher-level feature information to suppress the low-level feature and noises. Experimental results based on public international society for photogrammetry and remote sensing (ISPRS) datasets with only red–green–blue (RGB) images demonstrated that the proposed DAN achieved a higher score (96.16% overall accuracy (OA), 92.56% F1 score, 90.56% mean intersection over union (MIOU), less training and response time and higher-quality value) when compared with other deep learning methods. View Full-Text
Keywords: building extraction; deep learning; attention mechanism; very high resolution; imagery building extraction; deep learning; attention mechanism; 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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Yang, H.; Wu, P.; Yao, X.; Wu, Y.; Wang, B.; Xu, Y. Building Extraction in Very High Resolution Imagery by Dense-Attention Networks. Remote Sens. 2018, 10, 1768.

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