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Sensors 2017, 17(11), 2487; https://doi.org/10.3390/s17112487

Village Building Identification Based on Ensemble Convolutional Neural 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
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 15 September 2017 / Revised: 25 October 2017 / Accepted: 26 October 2017 / Published: 30 October 2017
(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)
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Abstract

In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. View Full-Text
Keywords: Ensemble Convolutional Neural Networks; remote sensing; building detection; village mapping; multiscale feature learning Ensemble Convolutional Neural Networks; remote sensing; building detection; village mapping; multiscale feature learning
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Guo, Z.; Chen, Q.; Wu, G.; Xu, Y.; Shibasaki, R.; Shao, X. Village Building Identification Based on Ensemble Convolutional Neural Networks. Sensors 2017, 17, 2487.

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