| 快速高准确度云检测算法及其应用 |
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| 引用本文： | 单娜,郑天垚,王贞松.快速高准确度云检测算法及其应用[J].遥感学报,2009,13(6):1147-1162. |
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| 作者姓名： | 单娜 郑天垚 王贞松 |
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| 作者单位： | 1. 中国科学院,计算技术研究所,北京,100190;中国科学院,研究生院,北京,100190
2. 中国科学院,计算技术研究所,北京,100190 |
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| 摘 要： | 采用光谱阈值判别和纹理分析相结合的方法, 提出一种基于树状判别结构的快速高准确度云检测算法, 综合利用多个判别准则, 确定图像中云层覆盖情况, 与传统方法相比能够获得更高的分辨精确度。树状判别结构还能够在平均意义上显著提高算法运行效率。同时, 提出了一种改进的分形维数计算方法, 能够在不影响精确度的前提下, 使算法的运行效率提高5倍左右, 基本满足实时性的要求。所提出的云检测算法已在中巴地球资源卫星项目中实际应用, 实际测试结果表明, 该算法达到自动云检测的虚警概率小于5%, 漏警概率小于10%的工程要求。
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| 关 键 词： | 遥感卫星图像 云层检测 分形维数计算 灰度共生矩阵 树状判别结构 |
| 收稿时间： | 2008/6/20 0:00:00 |
| 修稿时间： | 2009/2/10 0:00:00 |
High-speed and high-accuracy algorithm for cloud detection and its application |
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| SHAN N,ZHENG Tian-yao and WANG Zhen-song.High-speed and high-accuracy algorithm for cloud detection and its application[J].Journal of Remote Sensing,2009,13(6):1147-1162. |
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| Authors: | SHAN N ZHENG Tian-yao and WANG Zhen-song |
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| Affiliation: | 1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; 2. Graduate University of Chinese Academy of Sciences, Beijing 100190, China;1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;;1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; |
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| Abstract: | The cloud cover is an important factor which lowers the remote sensing image quality, so real-time automatic cloud detection and effective rejection of high cloud coverage pictures are of prime importance. In this paper we proposed a high performance and high accuracy algorithm of cloud detection which combines two different analytical techniques: the spectrum threshold comparison and the texture analysis. These two approaches discriminate the image from different visions. A structure of the discrimination tree is proposed to improve the accuracy and to accelerate the detecting procedure, which defines the rule how to use these two methods properly. The cloud detection results gained by this algorithm are well satisfied. And the structure of the discrimination tree promotes the operating efficiency on average. We also proposed an advanced approach to calculate the fractal dimension value, which is about five times faster than the original approach. The cloud detection algorithm has been applied to the data processing system of China-Brazil Earth Resources Satellite-2B. The experimental results show that this algorithm can satisfy the demand of error rate: the false alarm rate is lower than 5% and the missed detection rate is lower than 10%. |
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| Keywords: | satellite remote sensing image cloud detection fractal dimension calculation gray level co-occurrence matrix structure of the discrimination tree |
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