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Open AccessArticle

EU-Net: An Efficient Fully Convolutional Network for Building Extraction from Optical Remote Sensing Images

by Wenchao Kang 1,2,3,*, Yuming Xiang 1,2,3, Feng Wang 1,3 and Hongjian You 1,2,3
1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Huairou District, Beijing 101408, China
3
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2813; https://doi.org/10.3390/rs11232813
Received: 12 October 2019 / Revised: 15 November 2019 / Accepted: 25 November 2019 / Published: 27 November 2019
(This article belongs to the Special Issue Remote Sensing based Building Extraction)
Automatic building extraction from high-resolution remote sensing images has many practical applications, such as urban planning and supervision. However, fine details and various scales of building structures in high-resolution images bring new challenges to building extraction. An increasing number of neural network-based models have been proposed to handle these issues, while they are not efficient enough, and still suffer from the error ground truth labels. To this end, we propose an efficient end-to-end model, EU-Net, in this paper. We first design the dense spatial pyramid pooling (DSPP) to extract dense and multi-scale features simultaneously, which facilitate the extraction of buildings at all scales. Then, the focal loss is used in reverse to suppress the impact of the error labels in ground truth, making the training stage more stable. To assess the universality of the proposed model, we tested it on three public aerial remote sensing datasets: WHU aerial imagery dataset, Massachusetts buildings dataset, and Inria aerial image labeling dataset. Experimental results show that the proposed EU-Net is superior to the state-of-the-art models of all three datasets and increases the prediction efficiency by two to four times. View Full-Text
Keywords: building extraction; high-resolution aerial imagery; fully convolutional network; semantic segmentation building extraction; high-resolution aerial imagery; fully convolutional network; semantic segmentation
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MDPI and ACS Style

Kang, W.; Xiang, Y.; Wang, F.; You, H. EU-Net: An Efficient Fully Convolutional Network for Building Extraction from Optical Remote Sensing Images. Remote Sens. 2019, 11, 2813.

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