基于贝叶斯分类的两类天气图像自动识别
Automatic Identification of Two Kinds of Weather Image Based on Bayesian Classification
DOI: 10.12677/JISP.2015.44011, PDF,    国家自然科学基金支持
作者: 于 浩*, 王孝通, 徐冠雷：海军大连舰艇学院，辽宁 大连
关键词: 天气现象识别；图像处理；贝叶斯分类；Weather Phenomena Identification； Image Processing； Bayesian Classification
摘要: 为实现天气现象的自动观测，本文提出了一种基于贝叶斯分类的室外图像的天气现象识别方法，该方法通过提取图像的色相、饱和度和亮度等特征，采用贝叶斯分类，对雾霾和沙尘两类天气现象进行判断。实验结果的准确率达到预期目标，并且能快速、准确地测量室外图像的天气现象。
Abstract: To realize the automatic observation of weather phenomena, a method for recognizing weather phenomena of outdoor image based on Bayesian classification is proposed. This method extracts image characteristics of hue, saturation and brightness, and adopts Bayesian classification to estimate the haze and sand-dust weather. The accuracy of experiment results reaches the expected aim, and the experimental results show that this method can quickly and accurately measure the weather phenomena of outdoor image.
文章引用：于浩, 王孝通, 徐冠雷. 基于贝叶斯分类的两类天气图像自动识别[J]. 图像与信号处理, 2015, 4(4): 94-100. https://dx.doi.org/10.12677/JISP.2015.44011

参考文献

[1] 李骞, 范茵, 张璟, 李宝强 (2011) 基于室外图像的天气现象识别方法. 计算机应用, 6, 1624-1627.
[2] 陈爱霞, 杜友福, 陈中举 (2015) 图像识别的应用现状和研究方向. 中国科技信息, 16, 1-2.
[3] 徐峰 (2015) 基于数据库的叶片识别系统. 产业与科技论坛, 12, 42-43.
[4] 张洁 (2014) 基于图像识别的农作物害虫诊断技术研究与应用. 博士论文, 中国科学技术大学, 合肥.
[5] 蒋良孝 (2009) 朴素贝叶斯分类器及其改进算法研究. 博士论文, 中国地质大学, 武汉.
[6] 董立岩, 苑森淼, 刘光远, 贾书洪 (2007) 基于贝叶斯分类器的图像分类. 吉林大学学报(理学版), 2, 249-253.
[7] 陈法敬, 矫梅燕, 等 (2011) 亚高斯贝叶斯预报处理器及其初步试验. 气象学报, 5, 872-882.
[8] Xu, G.L., Wang, X.T. and Xu, X.G. (2011) Improved bi-dimensional empirical mode decomposition based on 2d assisted signals analysis and application. IET Image Processing, 5, 205-221. http://dx.doi.org/10.1049/iet-ipr.2009.0158 [Google Scholar] [CrossRef] 
[9] 彭丽玲 (2013) 基于图像灰度的天气变化机理及应用初步研究. 硕士论文, 昆明理工大学, 昆明.
[10] Bayes, T.R. (1763) An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370-418. http://dx.doi.org/10.1098/rstl.1763.0053 [Google Scholar] [CrossRef] 
[11] Pearl, J. (1988) Probabilistic reasoning intelligent systems: Net-works of plausible inference. Morgan Kaufmmann, San Mateo, 235-238.
[12] Gonzalez, R.C. and Woods, R.E. (2002) Digital image processing. 2nd Edition, Prentice Hall, Upper Saddle River.
[13] Katsura, H., Miura, J., Hild, M. and Shirai, Y. (2003) A view based outdoor navigation using object recognition robust to changes of weather and seasons. Proceedings of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, October 2003, 2974-2979. http://dx.doi.org/10.1109/IROS.2003.1249323 [Google Scholar] [CrossRef] 
[14] Roser, M. and Moosmann, F. (2008) Classification of weather situations on single color images. 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, 4-6 June 2008, 798-803. http://dx.doi.org/10.1109/IVS.2008.4621205 [Google Scholar] [CrossRef] 
[15] Yan, X., Luo, Y. and Zheng, X. (2009) Weather recognition based on images captured by vision system in vehicle. Lecture Notes in Computer Science, 5553, 390-398. http://dx.doi.org/10.1007/978-3-642-01513-7_42 [Google Scholar] [CrossRef]