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Remote Sensing Technology and Application
Convolutional Neural Network for RemoteSensing Plant Cover Extracting
Tian Deyu1,2,Zhang Yaonan1,3,Zhao Guohui1,2,3,Han Liqin1,2
(1.Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China;
2.University of Chinese Academy of Sciences,Beijing 100049,China;
3.Lanzhou Supercomputing Center of Chinese Academy of Sciences,Lanzhou 730000,China)
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The key point of the state-of-the-art machine learning method to extract land information is to construct the features-vector.The existing methods mainly use the spectral features,texture features of remote sensing images to construct the features-vector,however,this method can only get limited features and requires too much human intervention.In the face of the above problems,this paper builds a convolutional neural network model for mining the deep-level features of multi-band remote sensing images and then extract the greenbelt in the Kubuqi Desert.The model was trained and hyperparameter selection was performed.The performance of the model was evaluated by cross validation and comparative analysis between methods.The experimental results show that the model is of high accuracy and good generalization ability.Finally,the test data set was input into the model to predict land cover classes and to do visualization.The importance of this study is to inspire new thinking of better performance of the green land and even more complex information extraction from remote sensing images.
Key words:  Convolutional Neural Network      Feature Vector      Multi-band remote sensing      Information mining      Kubuqi desert     
Received:  17 December 2016      Published:  16 March 2018
TP 79  
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Tian Deyu
Zhang Yaonan
Zhao Guohui
Han Liqin

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Tian Deyu,Zhang Yaonan,Zhao Guohui,Han Liqin . Convolutional Neural Network for RemoteSensing Plant Cover Extracting. Remote Sensing Technology and Application, 2018, 33(1): 151-157.

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