计算机工程 ›› 2019, Vol. 45 ›› Issue (5): 199-204,209.doi: 10.19678/j.issn.1000-3428.0050628

• 图形图像处理 • 上一篇    下一篇

ILBP算子在浓雾天气形势图识别中的应用

陈文兵1,2,毛军杰1,陈允杰1,周林义2   

  1. 1.南京信息工程大学 数学与统计学院,南京 210044; 2.中国气象局 交通气象重点实验室,南京 210009
  • 收稿日期:2018-03-06 出版日期:2019-05-15 发布日期:2019-05-15
  • 作者简介:陈文兵(1964—),男,副教授,主研方向为图像处理、模式识别;毛军杰,硕士研究生;陈允杰,教授、博士;周林义,工程师。
  • 基金项目:

    国家自然科学基金(61672291);江苏省气象局北极阁基金(BJG201504)。

Application of ILBP Operator in Recognition of Fog Weather Situation Map

CHEN Wenbing1,2,MAO Junjie1,CHEN Yunjie1,ZHOU Linyi2   

  1. 1.College of Mathematics and Statistics,Nanjing University of Information Science and Technology,Nanjing 210044,China; 2.Key Laboratory of Transportation Meteorology,China Meteorological Administration,Nanjing 210009,China
  • Received:2018-03-06 Online:2019-05-15 Published:2019-05-15

摘要:

纹理是天气形势图的突出特征,有效地从天气形势图提取并表示其纹理是实现雾型实时在线预报的基础。基于此,提出一种改进的局部二值模式算法,通过调整局部二值模式(LBP)算子中二进制多项式的权重,实现其提取特定方向上纹理特征的目标。将江苏地区2010年—2017年500张浓雾天气形势图作为数据集,采用Chi统计法匹配测试数据与基准数据的相似度进行天气分类。实验结果表明,该算法的准确率、虚警率及临界成功指数分别为0.884、0.15和0.76,均优于LBP算法,具有较高的识别准确性与可靠性。

关键词: 改进的局部二值模式, 纹理特征, 特征提取, 图像匹配, 天气形势图

Abstract:

Texture is the salient feature of a weather situation map.Effectively extracting the textures from the weather situation map and representing them is the basis for real-time online prediction of fog pattern.To this end,this paper proposes an Improved Local Binary Pattern (ILBP) algorithm,which can extract texture features in a specific direction by adjusting the weight coefficients of binary polynomials in Local Binary Mode (LBP) operators.500 fog weather situation maps in Jiangsu Province from 2010 to 2017 are used as the dataset,and the similarities between the test data and the benchmark data are matched by Chi statistical method to classify the weather.Experimental results show that,the Probability of Detection (PoD),False Alarm Rate (FAR) and Critical Success Index (CSI) of the proposed algorithm are 0.884,0.15 and 0.76,respectively,which are better than those of the LBP algorithm.It proves that the proposed model has high accuracy and reliability.

Key words: Improved Local Binary Pattern (ILBP), texture feature, feature extraction, image matching, weather situation map

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