Computer Science ›› 2018, Vol. 45 ›› Issue (4): 227-232.doi: 10.11896/j.issn.1002-137X.2018.04.038

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Satellite Imagery Cloud Fraction Based on Deep Extreme Learning Machine

WENG Li-guo, KONG Wei-bin, XIA Min and CHOU Xue-fei   

  • Online:2018-04-15 Published:2018-05-11

Abstract: Cloud fraction is the key point for the application of satellite imagery.The existing methods cannot make full use of characteristics of satellite imagery,resulting in ineffective cloud detection and cloud fraction.In this paper,multi-layer neural network was used to extract the feature of satellite cloud image,and and through a large number of experiments,the best structure of depth learning network was found.This paper used deep extreme learning machine to detect and classify the cloud of satellite cloud image,and then used spatial correlation method to calculate the total cloud fraction.The results show that the deep extreme learning machine based on traditional extreme learning machine can extract the features of cloud images effectively,and can distinguish the boundary between thick cloud and thin cloud well.The cloud classification and cloud fraction accuracy of deep extreme learning machine are better than traditional thresho-ld method,extreme learning machine and convolutional neural network.

Key words: Cloud fraction,Deep extreme learning machine,Cloud detection,Spatial correlation,Satellite imagery

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