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Automatic Building Segmentation of Aerial Imagery Using Multi-Constraint Fully Convolutional Networks

1
Center for Spatial Information Science, University of Tokyo, Kashiwa 277-8568, Japan
2
Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 407; https://doi.org/10.3390/rs10030407
Received: 21 December 2017 / Revised: 21 February 2018 / Accepted: 3 March 2018 / Published: 6 March 2018
(This article belongs to the Special Issue Deep Learning for Remote Sensing)
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Abstract

Automatic building segmentation from aerial imagery is an important and challenging task because of the variety of backgrounds, building textures and imaging conditions. Currently, research using variant types of fully convolutional networks (FCNs) has largely improved the performance of this task. However, pursuing more accurate segmentation results is still critical for further applications such as automatic mapping. In this study, a multi-constraint fully convolutional network (MC–FCN) model is proposed to perform end-to-end building segmentation. Our MC–FCN model consists of a bottom-up/top-down fully convolutional architecture and multi-constraints that are computed between the binary cross entropy of prediction and the corresponding ground truth. Since more constraints are applied to optimize the parameters of the intermediate layers, the multi-scale feature representation of the model is further enhanced, and hence higher performance can be achieved. The experiments on a very-high-resolution aerial image dataset covering 18 km 2 and more than 17,000 buildings indicate that our method performs well in the building segmentation task. The proposed MC–FCN method significantly outperforms the classic FCN method and the adaptive boosting method using features extracted by the histogram of oriented gradients. Compared with the state-of-the-art U–Net model, MC–FCN gains 3.2% (0.833 vs. 0.807) and 2.2% (0.893 vs. 0.874) relative improvements of Jaccard index and kappa coefficient with the cost of only 1.8% increment of the model-training time. In addition, the sensitivity analysis demonstrates that constraints at different positions have inconsistent impact on the performance of the MC–FCN. View Full-Text
Keywords: aerial imagery; building detection; convolutional neural network; multi-constraint fully convolutional networks; feature pyramid aerial imagery; building detection; convolutional neural network; multi-constraint fully convolutional networks; feature pyramid
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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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Wu, G.; Shao, X.; Guo, Z.; Chen, Q.; Yuan, W.; Shi, X.; Xu, Y.; Shibasaki, R. Automatic Building Segmentation of Aerial Imagery Using Multi-Constraint Fully Convolutional Networks. Remote Sens. 2018, 10, 407.

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