Improving Cloud Detection in WFV Images Onboard Chinese GF-1/6 Satellite

We have developed an algorithm for cloud detection in Chinese GF-1/6 satellite multispectral images, allowing us to generate cloud masks at the pixel level. Due to the lack of shortwave infrared and thermal infrared bands in the Chinese GF-1/6 satellite, bright land surfaces and snow are frequently...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-11, Vol.15 (21), p.5229
Main Authors: Chang, Hao, Fan, Xin, Huo, Lianzhi, Hu, Changmiao
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Cameras
cloud detection
Clouds
Contamination
Data retrieval
Deep learning
Fmask
GF-1
Image processing
MODIS
Morphology
Mountain regions
Mountainous areas
Mountains
Pixels
Remote sensing
Satellite imagery
Satellites
Short wave radiation
Snow
Snow cover
Teaching methods
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Summary:We have developed an algorithm for cloud detection in Chinese GF-1/6 satellite multispectral images, allowing us to generate cloud masks at the pixel level. Due to the lack of shortwave infrared and thermal infrared bands in the Chinese GF-1/6 satellite, bright land surfaces and snow are frequently misclassified as clouds. To mitigate this issue, we utilized MODIS standard snow data products for reference data to determine the presence of snow cover in the images. Subsequently, our algorithm was utilized to correct misclassifications in snow-covered mountainous regions. The experimental area selected was the perpetually snow-covered Western mountains in the United States. The results indicate the accurate labeling of extensive snow-covered areas, achieving an overall cloud detection accuracy of over 91%. Our algorithm enables users to easily determine whether pixels are affected by cloud contamination, effectively improving accuracy in annotating data quality and greatly facilitating subsequent data retrieval and utilization.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15215229