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Open AccessArticle

BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images

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The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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The School of Remote Sensing Information Engineering, Wuhan University, Wuhan 430079, China
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The National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan 430072, China
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 1050; https://doi.org/10.3390/rs12061050
Received: 26 February 2020 / Revised: 19 March 2020 / Accepted: 21 March 2020 / Published: 24 March 2020
(This article belongs to the Section Remote Sensing Image Processing)
Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder–decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints. View Full-Text
Keywords: building residual refine network; convolutional neural network; building extraction; high resolution; remote sensing images building residual refine network; convolutional neural network; building extraction; high resolution; remote sensing images
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Shao, Z.; Tang, P.; Wang, Z.; Saleem, N.; Yam, S.; Sommai, C. BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images. Remote Sens. 2020, 12, 1050.

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