基于卷积神经网络的卫星云图云量计算

doi:10.16182/j.issn1004731x.joss.201805001

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (5): 1623-1630.doi: 10.16182/j.issn1004731x.joss.201805001

• 仿真建模理论与方法 • 下一篇

基于卷积神经网络的卫星云图云量计算

夏旻^1,2, 申茂阳^1,2, 王舰锋¹, 王阳光¹

1. 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 210044;
2. 南京信息工程大学江苏省大数据分析技术重点实验室,南京 210044

收稿日期:2016-06-01 修回日期:2016-08-04 出版日期:2018-05-08 发布日期:2019-01-03
作者简介:夏旻(1983-),男,江苏东台,博士,副教授,研究方向为机器学习和智能计算。
基金资助:
国家自然科学基金(61532009),江苏省六大人才高峰基金(2014-XXRJ-007),江苏省自然科学基金(BK20161533)

Cloud Fraction of Satellite Imagery Based On Convolutional Neural Networks

Xia Min^1,2, Shen Maoyang^1,2, Wang Jianfeng¹, Wang Yangguang¹

1.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China;
2. B-DAT, Nanjing University of Information Science and Technology, Nanjing 210044, China

Received:2016-06-01 Revised:2016-08-04 Online:2018-05-08 Published:2019-01-03

摘要/Abstract

摘要： 卫星云图云量计算是卫星气象应用的基础,目前的方法对卫星光学参数以及对卫星云图的特征利用率不高,导致了云检测及云量计算不准确。针对这个问题,利用卷积神经网络进行卫星云图云的检测,基于优化的卷积神经网络将云图分为厚云、薄云及晴空。在云检测的基础上利用“空间相关法”计算总云量。针对中国HJ-1A/B卫星图片的实验结果表明,通过对卷积网络结构及参数的优化卷积神经网络可以很好的提取云图的特征,云分类时厚云和薄云之间的过渡区域清晰,云的识别率以及云量计算的准确率都比传统阈值法、动态阈值法以及极限学习机模型的结果要好。

关键词: 云量计算, 卷积神经网络, 云检测, 空间相关法

Abstract: Cloud fraction is the basis for the application of meteorological satellite. Existing methods cannot use all the characteristics and optical parameters of the satellite cloud, which results in the inaccuracy of cloud detection and cloud fraction. In order to solve this problem, convolutional neural network is used for cloud detection. Based on the improved convolutional neural network, the satellite cloud image is divided into thin cloud, thick cloud and clear sky. Based on the cloud detection, an improved spatial correlation method is used for cloud fraction. The results for Chinese HJ-1A/B satellite imagery show that convolutional neural network can extract the features of cloud images effectively by optimizing the network structure and parameters, and the transition region between the thin cloud and thick cloud is clear for cloud classification. The simulation results show that the cloud classification and cloud fraction accuracy is better than traditional threshold, dynamic threshold method and extreme learning machine.

Key words: cloud fraction, convolutional neural network, cloud detection, spatial correlation

中图分类号:

TP183

夏旻, 申茂阳, 王舰锋, 王阳光. 基于卷积神经网络的卫星云图云量计算[J]. 系统仿真学报, 2018, 30(5): 1623-1630.

Xia Min, Shen Maoyang, Wang Jianfeng, Wang Yangguang. Cloud Fraction of Satellite Imagery Based On Convolutional Neural Networks[J]. Journal of System Simulation, 2018, 30(5): 1623-1630.

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基于卷积神经网络的卫星云图云量计算

Cloud Fraction of Satellite Imagery Based On Convolutional Neural Networks

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