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Remote Sensing Technology and Application  2022, Vol. 37 Issue (2): 368-378    DOI: 10.11873/j.issn.1004-0323.2022.2.0368
Evaluating the Potential of JL1 Remote Sensing Data in Urban Land Cover Classification Using Convolutional Neural Networks
Lü Dongmei1(),Yue Ma2,3(),Huapeng Li3
1.School of Electrical and Computer Engineering,Jilin Jianzhu University,Changchun 130118,China
2.School of Geomatics and Prospecting Engineering,Jilin Jianzhu University,Changchun 130118,China
3.Northeast Institute of Geography and Agroecology,CAS,Changchun 130102,China
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This research classified urban land cover using the Convolutional Neural Network (CNN) model based on the fine spatial resolution remotely sensed imagery from recently launched JL1 07B satellite. We applied CNN to classify imagery using different combinations of spectral feature variables, and compared the performance of CNN with three other methods, namely maximum likelihood classification algorithm, multi-layer perceptron algorithm and support vector machine algorithm. The experimental results demonstrated that CNN consistently achieved the highest overall accuracy (>90%), larger than that of other methods by above 12%, and reduced significantly the “salt-and-pepper” noise. The contribution of red-edge band to the classification accuracy was slight, while the near-infrared (NIR) band could increase the OA prominently. Overall, the effect of red-edge and near-infrared bands exerted a slightly impact on the OA of CNN, demonstrating the robustness and generalization of the CNN model. The high accuracy urban land cover classification map achieved using CNN based on JL1 satellite imagery can support the decision makings for land resource allocation, urban planning and regional administration.

Key words:  JL1 satellite      Convolutional neural network      Land cover      Deep learning      Fine spatial resolution imagery     
Received:  13 January 2021      Published:  17 June 2022
ZTFLH:  P237  
Corresponding Authors:  Yue Ma     E-mail:;
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Lü Dongmei
Yue Ma
Huapeng Li

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Lü Dongmei,Yue Ma,Huapeng Li. Evaluating the Potential of JL1 Remote Sensing Data in Urban Land Cover Classification Using Convolutional Neural Networks. Remote Sensing Technology and Application, 2022, 37(2): 368-378.

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Fig.1  Study area and key experimental regions
Fig.2  Model structure of CNN
合计3 5363 5063 6033 521
Table 1  Descriptions of samples
Table 2  Comparison of overall accuracy and Kappa between different classification methods
地物类型S1实验区 PA/%地物类型S2实验区 PA/%
Table 3  Comparison of produce′s accuracy between different classification methods
总和5415013713094382215133033093 506
Kappa0.847 4
Table 4  Confusion matrix of CNN model in S1 zone
总数5073524852934834583113013043 494
Kappa0.903 5
Table 5  Confusion matrix of CNN model in S2 zone
Fig.3  Comparison of overall accuracy and Kappa between different groups
Fig.4  Variation rates of Produce′s accuracy for different classes
Fig.5  Urban land cover classification maps achieved using different methods
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