Classification and detection of cloud, snow and fog in remote sensing images based on random forest

doi:10.6046/gtzyyg.2020074

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Abstract

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

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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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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020074 OR https://www.gtzyyg.com/EN/Y2021/V33/I1/96

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

Tab.1 Confusion matrix of the first detection results

Tab.2 Confusion matrix of the second detection results

Tab.3 Cloud, snow, fog detection accuracy of each satellite remote sensing images

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