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Remote Sensing Technology and Application  2020, Vol. 35 Issue (4): 767-774    DOI: 10.11873/j.issn.1004-0323.2020.4.0767
    
U-Net Neural Networks and Its Application in High Resolution Satellite Image Classification
Rui Yang1,2(),Yuan Qi1(),Yang Su1,2
1.Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract  

High-resolution remote sensing images have precise geometric structure and spatial layout, but the spectral information is limited, which increases the difficulty of classifying similar features of spectral features. Aiming at the problem of high resolution remote sensing image classification, a U-Net convolutional neural network classification method based on deep learning is proposed. Based on the remote sensing image of the Ejina Oasis GF-2 in the lower reaches of the Heihe River, the U-Net model was used to extract the five types of land cover types of Populus euphratica, Tamarix chinensis, cultivated land, grassland and bare land. The overall classification accuracy and Kappa coefficient were 85.024% and 0.795 6 respectively. Compared with the traditional Support Vector Machine(SVM) and object-oriented method, the results show that compared with SVM and object-oriented method, the U-Net model is used to classify the high-resolution remote sensing, and the classification effect is better. The ground extracts the essential features of the features to meet the accuracy requirements.

Key words:  Deep learning      U-Net model      Gaofen-2 remote sensing image      SVM      Classification     
Received:  29 January 2019      Published:  15 September 2020
ZTFLH:  TP75  
Corresponding Authors:  Yuan Qi     E-mail:  yangrui@lzb.ac.cn;qiyuan@lzb.ac.cn
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Rui Yang
Yuan Qi
Yang Su

Cite this article: 

Rui Yang,Yuan Qi,Yang Su. U-Net Neural Networks and Its Application in High Resolution Satellite Image Classification. Remote Sensing Technology and Application, 2020, 35(4): 767-774.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2020.4.0767     OR     http://www.rsta.ac.cn/EN/Y2020/V35/I4/767

Fig.1  The whole structure of U-Net neural networks
Fig.2  The process of two-dimensional convolution
Fig.3  The sample selection of datasets
Fig.4  Flow chart of U-Net classification
Fig.5  GF-2 standard false color image and classification results
Fig.6  Three typical image subsets (A, B and C) with their classification results in study area
Fig.7  The curves of loss function and accuracy versus iteration epochs

深度学习

网络模型

SVM/%面向对象/%U-Net/%
Kappa0.726 40.753 00.795 6
裸地74.7084.3490.09
胡杨75.3381.3386.50
耕地76.3683.0682.56
柽柳82.3573.9971.01
草地85.1587.3189.59
总体精度79.02882.05285.024
Table 1  The table of accuracy assessment for classification
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