基于深度极限学习机的卫星云图云量计算
发布时间：2018-11-03 15:38
【摘要】：卫星云图云量计算是卫星气象应用的基础,现阶段对其的研究未能充分利用卫星云图的特征,导致云检测及云量计算的效果不好。针对该问题,利用多层神经网络进行卫星云图的特征提取,并通过大量实验寻找到最优的深度学习的网络结构。基于度极限学习机对卫星云图的云进行检测和分类,再利用"空间相关法"计算云图中的总云量。实验结果表明,基于传统极限学习机的深度极限学习机能够充分提取云图的特征,在进行云分类时能够较清晰地区分厚云和薄云间的界限。相比于传统阈值法、极限学习机模型以及卷积神经网络,深度极限学习机的云识别率以及云量计算准确率更高,且所提方法比卷积神经网络的效率更高。
[Abstract]:The cloud volume calculation of satellite cloud image is the basis of satellite meteorological application. At present, the research on it fails to make full use of the characteristics of satellite cloud image, which leads to the poor effect of cloud detection and cloud amount calculation. To solve this problem, the multi-layer neural network is used to extract the features of satellite cloud images, and the optimal network structure of depth learning is found through a large number of experiments. Based on the degree limit learning machine, the cloud of satellite cloud image is detected and classified, and the total cloud amount in the cloud image is calculated by "spatial correlation method". The experimental results show that the depth extreme learning machine based on the traditional extreme learning machine can fully extract the features of cloud images and can clearly distinguish the boundary between thick cloud and thin cloud in cloud classification. Compared with the traditional threshold method, the cloud recognition rate and cloud volume calculation accuracy of the depth ultimate learning machine are higher than that of the traditional threshold method, and the proposed method is more efficient than the convolution neural network.
【作者单位】： 南京信息工程大学江苏省大气环境与装备技术协同创新中心 南京信息工程大学江苏省大数据分析重点实验室
【基金】：国家自然科学基金(61503192) 江苏省自然科学基金(BK20161533) 江苏省六大人才高峰高层次人才资助计划(2014-XXRJ-007)资助
【分类号】：TP181;TP391.41
，
本文编号：2308216
[Abstract]:The cloud volume calculation of satellite cloud image is the basis of satellite meteorological application. At present, the research on it fails to make full use of the characteristics of satellite cloud image, which leads to the poor effect of cloud detection and cloud amount calculation. To solve this problem, the multi-layer neural network is used to extract the features of satellite cloud images, and the optimal network structure of depth learning is found through a large number of experiments. Based on the degree limit learning machine, the cloud of satellite cloud image is detected and classified, and the total cloud amount in the cloud image is calculated by "spatial correlation method". The experimental results show that the depth extreme learning machine based on the traditional extreme learning machine can fully extract the features of cloud images and can clearly distinguish the boundary between thick cloud and thin cloud in cloud classification. Compared with the traditional threshold method, the cloud recognition rate and cloud volume calculation accuracy of the depth ultimate learning machine are higher than that of the traditional threshold method, and the proposed method is more efficient than the convolution neural network.
【作者单位】： 南京信息工程大学江苏省大气环境与装备技术协同创新中心 南京信息工程大学江苏省大数据分析重点实验室
【基金】：国家自然科学基金(61503192) 江苏省自然科学基金(BK20161533) 江苏省六大人才高峰高层次人才资助计划(2014-XXRJ-007)资助
【分类号】：TP181;TP391.41
，
本文编号：2308216
本文链接：https://www.wllwen.com/kejilunwen/zidonghuakongzhilunwen/2308216.html
