【作者】 邓集萱；

【导师】 程永强；

【作者基本信息】 太原理工大学 ， 电子与通信工程， 2012， 硕士

【摘要】 进行云分类和云团分析预测的技术与方法研究,客观、准确、及时地进行云图识别和云区域估计等研究,是气象学应用中主要的研究方向。论文基于学科与方法交叉的思想,运用数字图像处理技术展开云图分析及云层面积测量的研究,在分析和测量过程中给出了几种有效的算法。论文首先对气象云图预处理,给出一种去除下垫面的新方法——直方图阈值双峰法。该方法利用样本云与下垫面在图像中灰度差异大的特点,达到样本云与下垫面的有效分割。在去除薄雾噪声时,针对薄雾变化比较平缓,对应较低的频率分量的特点,运用了同态滤波法。实验结果表明,预处理后的云图图像提高了质量,降低了复杂度,获得了较好的图像效果。数字图像预处理之后,论文在本章对样本云进行了进一步的分析。首先,针对传统PCA算法维数灾难问题,提出PCA的改进算法,减少压缩前后的均方误差,提高分辨力,在较低维数字图像空间得到清晰的样本云图,实现云图有效的识别。实验表明,图像SNR提高了7.64dB,识别率也大大提高。论文还对样本云的定量统计进行了讨论,给出了计数的基本思路和实现函数。算法在MATLAB中运行,仿真结果和传统的人工计数相比,不仅计数一样,而且操作很简单、省时。最后,针对样本云反射率测定现状,将图像处理技术和数学理论有机的融合,在反射率和可见光与红外亮度之间建立了数学关系,实现样本云分类的目的。另外,卫星云图或多或少存在云遮挡的“盲区”、信息丢失,对于云图分析与解译不利。这就引出了本文要解决的第二个问题——云层面积计算。将N*M像元素阵列的图象转换成许多个互相不重叠的区域组成的块的集合。采用图像处理技术对Freeman链码算法进行改进,只要对链码边界点进行判断,实现封闭区域的迅速填充,自动的计算出不规则云层的面积。实验结果表明,运算耗时小,精确度在±1%左右。总之,本文利用图像处理技术,为云图分析及云层面积测量提供了有效可行的解决途径,在已有理论的基础上优化和改进,为实际研究和应用提供了新思路。更多还原

【Abstract】 The study on the technology and methods for clouds classification and clouds analysis prediction, and the research on how to identify nephogram and estimate clouds regional objectively, precisely and timely, which are main research directions in the application technology on the meteorology. Based on cross-ideologies of disciplines and methods, this paper explores on nephogram analysis and clouds area measurement by using digital image processing technology and provides effective algorithm in the process of analysis and measurement.This paper first pre-treat nephogram and presents a new method of removing underlying surface——pad-histogram threshold value method of twin peaks. This method will make use of different gray-level between sample cloud and underlying surface in the picture to realize sample cloud and underlying surface segmentation efficiently. As for wiping off mist noise, homomorphic filtering method is used because the mist change is relatively flat, corresponding to the low frequency components. Experiment shows that pre-treat nephogram quality is improved while complexity lowered, a better image effect。After image pre-treatment, the paper carried out further analysis of the sample cloud in this chapter. First, aiming to solve the dimension disasters problems brought by the traditional PCA, this paper gives the advanced algorithm of PCA, reduces the mean-square-error in non-compressed and compressed situations and improves the resolution in order to obtain clear sample nephogram in low dimension space of digital image. Experiment shows that the image SNR increase by7.64dB and the image recognition rate also largely improved.The paper also discussed the quantitative statistics of the sample cloud, giving the basic ideas and realization of functions of count. The algorithm is run in MATLAB, the simulation results not only count the same, but also operation is very simple, time-saving when it compares to the traditional manual. Finally, according to the current situation of sample cloud Reflectance measurement, this paper combined image processing techniques and mathematical theory organically, established mathematical relationships between the reflectivity, visual light and infrared brightness, achieving the purpose of the sample cloud classification.In addition, satellite nephogram exists more or less "blind area" of cloud cover causing information lost which harm nephogram analysis and interpretation. This brings up a second question in this paper to solve——calculating clouds area. Making the image of N*M element in array into a set of blocks made of many non-overlapping regions. Using image processing technology will improve Freeman chain code algorithm, only judgying the boundary points of the chain code, achieving the rapid filling of the enclosed area in order to calculate the area of irregular clouds automatically. The experimental results show that the computing time-consuming small, accuracy achieves about±1%.In short, this paper presents effective and feasible solution for nephogram analysis and clouds area measurement by using image processing technology. The findings is based on previous theories, optimized and improved by this paper, providing new ideas for future theory study and application.更多还原