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Remote Sens. 2019, 11(8), 917; https://doi.org/10.3390/rs11080917

Building Extraction from High-Resolution Aerial Imagery Using a Generative Adversarial Network with Spatial and Channel Attention Mechanisms

1
School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China
2
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
3
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
4
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XW, UK
*
Author to whom correspondence should be addressed.
Received: 18 March 2019 / Revised: 12 April 2019 / Accepted: 12 April 2019 / Published: 15 April 2019
(This article belongs to the Special Issue Remote Sensing based Building Extraction)
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Abstract

Segmentation of high-resolution remote sensing images is an important challenge with wide practical applications. The increasing spatial resolution provides fine details for image segmentation but also incurs segmentation ambiguities. In this paper, we propose a generative adversarial network with spatial and channel attention mechanisms (GAN-SCA) for the robust segmentation of buildings in remote sensing images. The segmentation network (generator) of the proposed framework is composed of the well-known semantic segmentation architecture (U-Net) and the spatial and channel attention mechanisms (SCA). The adoption of SCA enables the segmentation network to selectively enhance more useful features in specific positions and channels and enables improved results closer to the ground truth. The discriminator is an adversarial network with channel attention mechanisms that can properly discriminate the outputs of the generator and the ground truth maps. The segmentation network and adversarial network are trained in an alternating fashion on the Inria aerial image labeling dataset and Massachusetts buildings dataset. Experimental results show that the proposed GAN-SCA achieves a higher score (the overall accuracy and intersection over the union of Inria aerial image labeling dataset are 96.61% and 77.75%, respectively, and the F1-measure of the Massachusetts buildings dataset is 96.36%) and outperforms several state-of-the-art approaches. View Full-Text
Keywords: high-resolution aerial images; deep learning; generative adversarial network; semantic segmentation; Inria aerial image labeling dataset; Massachusetts buildings dataset high-resolution aerial images; deep learning; generative adversarial network; semantic segmentation; Inria aerial image labeling dataset; Massachusetts buildings dataset
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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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Pan, X.; Yang, F.; Gao, L.; Chen, Z.; Zhang, B.; Fan, H.; Ren, J. Building Extraction from High-Resolution Aerial Imagery Using a Generative Adversarial Network with Spatial and Channel Attention Mechanisms. Remote Sens. 2019, 11, 917.

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