Evaluating the Potential of JL1 Remote Sensing Data in Urban Land Cover Classification Using Convolutional Neural Networks

doi:10.11873/j.issn.1004-0323.2022.2.0368

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 Ma^2,³(

),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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Abstract

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: dongmeilv@126.com;mayue417@hotmail.com

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Cite this article:

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.

URL:

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.2.0368 OR http://www.rsta.ac.cn/EN/Y2022/V37/I2/368

Fig.1 Study area and key experimental regions

Fig.2 Model structure of CNN

Table 1 Descriptions of samples

Table 2 Comparison of overall accuracy and Kappa between different classification methods

Table 3 Comparison of produce′s accuracy between different classification methods

Table 4 Confusion matrix of CNN model in S1 zone

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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