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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (1) : 96-101     DOI: 10.6046/gtzyyg.2020074
Classification and detection of cloud, snow and fog in remote sensing images based on random forest
XU Yun1(), XU Aiwen2
1. Hangzhou Transportation Planning and Design Institute, Hangzhou 310003, China
2. Zhejiang Academy of Land and Space Planning, Hangzhou 310012, China
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Cloud, snow and fog are important factors affecting the quality of optics remote sensing images, and hence researchers should detect the range of cloud, snow, fog in remote sensing images and remove unwanted images so as to improve the utilization of remote sensing images. In this paper, the authors studied the method based on Random Forest to detect cloud, snow, fog and tried to reduce the error detection rate by means of adding a “second detection”. Experiments show that this method has high detection accuracy and efficiency.

Keywords random forest      classification detection of cloud, snow, fog      second detection     
ZTFLH:  P237  
Issue Date: 18 March 2021
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Yun XU
Aiwen XU
Cite this article:   
Yun XU,Aiwen XU. Classification and detection of cloud, snow and fog in remote sensing images based on random forest[J]. Remote Sensing for Land & Resources, 2021, 33(1): 96-101.
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Fig.1  Cloud, snow, fog sample of NAD and MUX image
Fig.2  Algorithm flowchart of cloud, snow, fog classification
Fig.3  Land feature contrast with cloud, snow, fog
Fig.4  Cloud, snow, fog classification results of each satellite
类别 地物/像素 云/像素 雾/像素 雪/像素 总计/像素
地物 6 737 349 112 378 93 557 104 046 7 047 330
175 042 898 072 55 769 15 071 1 143 954
100 075 86 752 214 309 1 437 402 573
228 746 8 250 86 586 876 823 958
总计 7 241 212 1 105 452 363 721 707 430 9 417 815
精度指标 总体分类精度: 89.6%; Kappa=0.741
Tab.1  Confusion matrix of the first detection results
类别 地物/像素 云/像素 雾/像素 雪/像素 总计/像素
地物 6 851 716 91 773 57 012 54 405 7 054 906
171 944 928 589 48 496 14 018 1 163 047
92 379 78 142 258 159 1 383 430 063
125 173 6 948 54 637 624 769 799
总计 7 241 212 1 105 452 363 721 707 430 9 417 815
精度指标 总体分类精度: 92.1%; Kappa=0.804
Tab.2  Confusion matrix of the second detection results
影像类别 传感器
ZY-3 NAD 1 023 934 995 91.3 97.3
ZY1-02C NAD 554 488 527 88.1 95.1
GF-1 MUX 832 752 797 90.4 95.8
TH01-01 MUX 317 278 296 87.8 93.4
Tab.3  Cloud, snow, fog detection accuracy of each satellite remote sensing images
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