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Remote Sens. 2016, 8(4), 271;

Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods

Center for Spatial Information Science, University of Tokyo, Kashiwa 277-8568, Japan
Earth Observation Data Integration and Fusion Research Initiative, University of Tokyo, Tokyo 153-8505, Japan
Author to whom correspondence should be addressed.
Academic Editors: Devrim Akca, Magaly Koch and Prasad S. Thenkabail
Received: 18 December 2015 / Revised: 10 March 2016 / Accepted: 17 March 2016 / Published: 25 March 2016
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In this study, a method based on supervised machine learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE) RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine learning methods to provide automatic classification in such fields. By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine learning methods for classification: adaptive boosting (AdaBoost) and convolutional neural networks (CNN). To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22% and the CNN method is also competitive with an overall accuracy of 96.30%. View Full-Text
Keywords: remote sensing; village mapping; Google Earth; CNN; AdaBoost remote sensing; village mapping; Google Earth; CNN; AdaBoost

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Guo, Z.; Shao, X.; Xu, Y.; Miyazaki, H.; Ohira, W.; Shibasaki, R. Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods. Remote Sens. 2016, 8, 271.

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