[1]石玉立,王 彬,卜 帆.基于图像特征的大气能见度估算方法[J].南京理工大学学报(自然科学版),2018,42(05):552.[doi:10.14177/j.cnki.32-1397n.2018.42.05.007]
 Shi Yuli,Wang Bin,Bu Fan.Atmospheric visibility measurement based on image feature[J].Journal of Nanjing University of Science and Technology,2018,42(05):552.[doi:10.14177/j.cnki.32-1397n.2018.42.05.007]
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基于图像特征的大气能见度估算方法()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
42卷
期数:
2018年05期
页码:
552
栏目:
出版日期:
2018-10-30

文章信息/Info

Title:
Atmospheric visibility measurement based on image feature
文章编号:
1005-9830(2018)05-0552-08
作者:
石玉立王 彬卜 帆
南京信息工程大学 遥感与测绘工程学院,江苏 南京 210044
Author(s):
Shi YuliWang BinBu Fan
School of Remote Sensing & Geomatics Engineering,Nanjing University of InformationScience & Technology,Nanjing 210044,China
关键词:
能见度 梯度 对比度 支持向量机 随机森林
Keywords:
visibility gradient contrast support vector machine random forest
分类号:
TP391.41
DOI:
10.14177/j.cnki.32-1397n.2018.42.05.007
摘要:
针对现有图像能见度测量方法存在目标物架设复杂和物理量解算困难的问题,该文提出一种简单有效的基于图像特征的大气能见度估算方法。采用图像梯度和对比度作为能见度的特征变量,将图像感兴趣区进行分窗处理后,利用支持向量机(SVM)和随机森林(RF)两种算法建立能见度数值与图像特征的关系模型。结果表明,图像梯度和对比度特征能够准确反映能见度数值大小; 窗口总数对模型精度有影响。窗口总数小于35时SVM算法优于RF算法; 在窗口总数为70、窗口大小为140×10时,RF算法的像素精度最高,最优模型决定系数R2为0.965,均方根误差为658.13 m。
Abstract:
In view of that the current methods of atmospheric visibility measurement based on the image have many problems,such as the complex erection of the target and difficult calculation of physical quantities,the atmospheric visibility measurement based on image features is proposed here. The gradient and contrast of the image are selected as image features. The support vector machine(SVM)algorithm and the random forest(RF)algorithm are used to build the model of the relationship between visibility values and image features. All results show that the gradient and contrast of the image are highly correlated with the atmospheric visibility value. The total number of sub-windows has effect on the accuracy of the model. The result of the SVM algorithm is typically better than that of the RF algorithm when the total number of sub-windows is less than 35. The sub-window with the size of 140×10 pixels performs best for the random forest algorithm. The R2 of the optimal model is 0.965 and the root-mean-square error is 658.13 m.

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备注/Memo

备注/Memo:
收稿日期:2018-03-19 修回日期:2018-06-26
基金项目:国家自然科学基金(41471312)
作者简介:石玉立(1973-),男,博士,教授,主要研究方向:机器学习,遥感图像处理,E-mail:ylshi.nuist@gmail.com。
引文格式:石玉立,王彬,卜帆. 基于图像特征的大气能见度估算方法[J]. 南京理工大学学报,2018,42(5):552-559.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2018-10-30