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《Remote Sensing Technology and Application》 2018-01
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Convolutional Neural Network for Remote Sensing Plant Cover Extracting

Tian Deyu;Zhang Yaonan;Zhao Guohui;Han Liqin;Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences;University of Chinese Academy of Sciences;Lanzhou Supercomputing Center of Chinese Academy of Sciences;  
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.
【Fund】: 国家自然科学重点基金项目(91125005/D011004);; 中国科学院信息化重点项目(INFO-115-D01)资助
【CateGory Index】: TP183;TP751
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