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

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 fe...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2016, Vol.8 (4), p.271-271
Main Authors: Guo, Zhiling, Shao, Xiaowei, Xu, Yongwei, Miyazaki, Hiroyuki, Ohira, Wataru, Shibasaki, Ryosuke
Format: Article
Language:English
Subjects:
AdaBoost
Buildings
Classification
CNN
Earth
Google Earth
Machine learning
Remote sensing
Texture
village mapping
Villages
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Summary: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%.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs8040271