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Remote Sens. 2016, 8(4), 271; doi:10.3390/rs8040271

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

1
Center for Spatial Information Science, University of Tokyo, Kashiwa 277-8568, Japan
2
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
View Full-Text   |   Download PDF [2436 KB, uploaded 25 March 2016]   |  

Abstract

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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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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MDPI and ACS Style

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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