| 基于局部阈值插值的地基云自动检测方法 |
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| 引用本文： | 杨俊,吕伟涛,马颖,姚雯,李清勇.基于局部阈值插值的地基云自动检测方法[J].气象学报,2010,68(6):1007-1017. |
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| 作者姓名： | 杨俊 吕伟涛 马颖 姚雯 李清勇 |
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| 作者单位： | 中国气象科学研究院大气探测研究所, 北京, 100081;中国气象科学研究院大气探测研究所, 北京, 100081;中国气象科学研究院大气探测研究所, 北京, 100081;中国气象科学研究院大气探测研究所, 北京, 100081;北京交通大学计算机与信息技术学院, 北京, 100044 |
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| 基金项目： | 中国气象科学研究院基本科研业务费专项项目，国家自然科学基金，国家科技部科研院所技术开发研究专项 |
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| 摘 要： | 地基云自动化观测是当前气象业务发展的迫切需求.目前的地基云检测算法仍主要是以阈值为基础,针对固定阈值和全局阈值算法在云检测精度方面存在的不足,利用晴朗天空下天空呈蓝色、云呈白色的属件,提出了一种基于局部阈值插值的地基云自动检测方法.该方法在对云图进行重采样后,对云图蓝、红波段进行归一化差值处理,再将处理后的结果图像按空间像素位置自动分成互不重叠、大小相等的均匀子块,对每一子区域采用一定的规则并结合改进的最大类间方差自适应阈值算法计算局部阈值,然后对每一子区域形成的阈值矩阵采用双线性插值算法进行插值处理,形成与原始云图大小相等的阈值曲面,利用此阈值曲面与云图蓝、红波段归一化差值处理结果进行比较,即可完成地基云的自动检测.与固定阈值和全局阈值算法相比,局部阈值插值算法对一些细碎的云和与背景反差不大的云获得了更好的检测效果.定量的评估结果表明,固定阈值方法在正确率和精确度上都要远远低于全局阈值和局部阈值方法,而文中提出的局部阈值算法在正确率和精确度上相比全局阈值算法又有较大提高.
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| 关 键 词： | 地基云 云检测 归一化差值 最大类间方差 局部阈值 |
| 收稿时间： | 2008/11/3 0:00:00 |
| 修稿时间： | 2009/3/13 0:00:00 |
An automatic ground based cloud detection method based on the local threshold interpolation |
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| YANG Jun,Lv Weitao,MA Ying,YAO Wen and LI Qingyong.An automatic ground based cloud detection method based on the local threshold interpolation[J].Acta Meteorologica Sinica,2010,68(6):1007-1017. |
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| Authors: | YANG Jun Lv Weitao MA Ying YAO Wen and LI Qingyong |
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| Institution: | YANG Jun,L(U) weitao,MA Ying,YAO Wen,LI Qingyong |
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| Abstract: | The automatic ground-based observation of cloud is an exigent requirement for the current meteorology operation. Up to now, the detection algorithms for the ground based cloud observation are mainly based on the threshold, but neither the fixed threshold nor the global threshold method can achieve satisfactory ground based cloud detection effects. Using the properties of blue sky background versus white cloud in clear sky, an automatic ground based cloud detection method is presented based on the local threshold interpolation in which the original cloud image is resized to an appropriate size and then the normalized difference operation is performed on the blue band and the red band of the resampled image. After that, the normalized difference result is separated into a series of quadrilled sub images according to the spatial position of the image pixels automatically. Next, the improved maximum interclass variance adaptive threshold algorithm and some decision making rules are used to compute the local threshold for each sub image, and,using the bilinear interpolated algorithm, the threshold array is interpolated to form a curved surface whose size is the same as the original cloud image. Finally, the curved threshold surface is used to finish ground based cloud detection by comparing to the normalized difference result of the blue and red bands pixel by pixel. Compared with the fixed threshold and the global threshold algorithms, the proposed method obtains better detection effects for clouds in small, broken bits and weak contrast clouds. The quantitative assessment results show that the fixed threshold algorithm has a much lower correctness and accuracy than the global threshold method and the local threshold method. Furthermore, the proposed method acquired better results than the global threshold algorithm both in correctness and accuracy. |
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| Keywords: | Ground based observation of cloud Cloud detection Normalized difference Maximum interclass variance Local threshold |
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