多光谱静止气象卫星云图的云类判别分析与短时移动预测
|
摘要
军事气象保障的复杂性促进了先进、实用的预报理论和保障技术的涌现。卫星云图蕴含着丰富的气象信息。客观、准确、实时地识别卫星云图中的云类特征、开展云团短时活动预测是军事气象保障中急待解决的重点和难点问题。本文针对云分类和云团短时移动的特殊性和复杂性,基于学科和方法交叉的思想,研究探索了卫星云图云类识别和云团短时预测的研究方法和技术途径。
本文研究内容大致分两部分。第一部分为多光谱卫星云图的云分类判别研究,主要内容包括:
(1)资料收集整理、误差订正和样本采集。对收集的GMS-5静止气象卫星的红外1、红外2、可见光和水汽四个通道的云图资料进行了灰度与分辨率的标准化转换和太阳高度角订正;从中采集多种典型云类和陆地、水体的若干样本,建立了云类样本库,为随后的云分类研究提供建模和实验的数据信息。
(2)季节内的云分类判别。季节内云的特征识别与分类是日常气象保障的基本内容,针对当前云分类研究中广泛采用的特征空间映射分类法调控性差、云类样本要求高、噪声误差需要人工判断消除等缺点以及常规人工神经网络分类模型对云图梯度和纹理特征描述不足等问题,通过计算云图样本灰度-梯度共生矩阵及其若干特征统计量,用BP神经网络建立了云分类模型。该模型可综合利用云图样本的多种特征信息(如灰度、梯度和纹理),在特征量输入与分类输出间建立起良好的非线性映射关系;通过在网络训练中引入模糊化处理方法,减小了网络复杂程度、加速了网络收敛,分类结果更为准确、合理。所建BP网络分类器模型能够对云-地之间、云-水之间和季节内的多种典型云类进行有效的分类判别。
(3)年际内的云分类判别。在实际业务应用中,需要考虑全年各个季节的云分类问题,即年际尺度和季节间的云分类判别。由于云生成和维持的复杂性和多样性,不同季节的云类主体的形态特征存在较大差异,因此笼统采用全年资料序列建模的模型训练难度大、误差收敛慢、泛化性能差,尤其对季节转换期间的云分类判别效果更差。针对上述问题,本文基于自组织特征映射网络(SOMF)和概率神经网络(PNN)各自优势,提出SOMF网络与PNN网络结合的二级分离云分类方法。该方法将云分类过程分为两个阶段:首先用自组织特征映射网络对样本特征库进行无监督聚类(一级分类);其后再针对分离出的各类样本进行有监督训练和目标修正,分别建立其各自的概率神经网络的云分类模型(二级分类)。上述处理方法不是机械地按照季节划分,而是遵循云类样本特征空间结构的自然相似性进行客观合理的分类建模,既考虑了不同季节云类主体的差异,又简化了云分类过程复杂性,使得季节过渡和转换期间的云分类模型建立和选择有了客观依据,云分类准确率得到了有效提高。
(4)云类过渡区的多属性判别。针对各云类过渡区间存在的多属性(同时具有多种云类特征)问题和云类样本中的误差问题,基于云类样本的红外-可见光二维灰度特征空间,提出了用模糊C均值聚类(FCM)方法调整优化云类样本特征映射集的技术途径。该方法以隶属度为度量,较为合理地描述了云图空间点(尤其是云类过渡区空间点)的多云类属性和云类特征的“模糊”性,其“柔性”分类结果更贴近客观实际和人们的视觉理解,同时既有效地降低了采样误差,又保持了云类样本的基本结构特征。针对常规FCM方法对聚类初值的敏感性,提出用样本特征均值替代常规FCM方法中随机的初始中心的改进办法,改善了常规模糊C均值聚类方法难以检测不规则云结构特征子集的不足。
(5)非典型云类和多层云系的分类判别。实际天气中,除了特征较为明显的“典型”云类外,还存在大量特征较为“模糊”的云系(如天气系统生消演变期间和多种天气系统相互共存等情况下的云类)。针对实际天气中上述非典型云类和多层云系的“模糊性”分类问题,以及常规模糊聚类方法对初始聚类中心选取的随机性和聚类结果的“局部优化”等问题,本文提出遗传算法(GA)全局寻优与模糊C均值聚类(FCM)局部寻优以及模糊减法聚类(FSC)客观估算聚类数等优势互补的研究思想和技术途径。首先用FSC方法客观确定云图中的云类数,其后基于估算的云类数,采用GA进行全局搜索,确定初始聚类中心,最后用FCM方法对GA提供的聚类结果再进行局部的调整优化。试验结果表明,综合云分类方法(FSC-GA-FCM)的分类效果明显优于单一的FCM或GA算法,表现出较好的创新特色和技术优势,并被有效应用于相关科研项目的云分类算法设计之中。
第二部分为卫星云图中云团移动的短时预测研究,主要内容包括:
(1)确定了基于局部小波矩方法的云团特征匹配和移动矢量计算方案。首先系统阐述了云团移动矢量计算的基本思想,重点引入和分析了图像处理中四种重要的匹配方法:傅立叶位相法、交叉相关系数法、局部信息熵方法和局部小波矩方法在云团特征匹配中的应用,并针对云团移动过程中强度变化和旋转等非平稳运动特点,设计了上述四种云团匹配计算方案,通过仿真模拟,分析、评估了各种算法的优缺点,发现局部小波矩方法能够更为合理恰当地表现云团环境的变化和特征匹配,进而为云团预测的运动矢量计算提供了可靠的计算方案。此外,还提出了移动矢量平滑处理和质量控制的改进方法。
(2)建立了基于云团移动矢量线性外推的云团短时预测模型。基于上述优选的云团运动矢量算法和Cressman多次连续平滑方法质量控制,结合后向轨迹方法,设计了云图1～3小时的短时预测模型。试验结果表明,该方法的云图1-3小时的预测结果可有效改善主观目测卫星云图判断云团移动的偏差,并可在一定程度上表现云的旋转等非平稳运动特性,进而为云团移动预测提供了一个客观、定量的技术手段。
(3)提出了经验正交函数分解与动力系统重构相结合的云图非线性预测思想。基于经验正交函数(EOF)方法对卫星云图的样本序列进行时、空分解;在EOF分解的空间场相对稳定的基础上,引入遗传算法对EOF分解的时间系数序列进行动力系统重构和模型参数反演,建立了云团演变的非线性动力预报模型。通过对EOF时间系数预测结果与空间结构模态的重构合成,实现了云图非平稳运动的中、长时效预测,为云图预测提供了一种创新思想方法和实用技术途径。
To compete with hardness of military meteorological service , it is forced to produce lots of advanced theories and practical technologies which can serve the purpose of weather forecast. A satellite cloud image contains a lot of weather information. It is hard and important task to distinguish the characteristics and short-time movement forecast of satellite cloud image timely、correctly、objectively and automatically. To meet the challenge of particularity and complexity of cloud classification and short-time movement forecast of satellite cloud image, it is studied to improve used theory and find a new arithmetic which can be easily put into use based on idea of crossing of subjects and methods in this paper.
The paper contains two parts. In part one, it is mainly aimed at distinguishing and classification of satellite cloud image. There are five subjects:
(1) After standard conversion of gray and recognizing ratio and as well as rectifying to the height angle of the Sun from satellite cloud image of GMS-5 through four bands of IRK IR2、VS and WV , it is built of a sample base which collects numbers of samples of land and water and seven types of typical clouds as well, which is used for study of cloud classification;
(2) Distinguishing and classification of cloud characteristics inner season is basic part of ordinary weather services. On the basis of above works, the samples of cloud classification are reflected to two dimension gray space of IR and VS by the means of Characteristic Space Projection (i.e. CSP). It is found the criteria of classification of cloud pixel with determination of falling area of the samples. The test results show that the method of CSP is simple and easy, especially suited for uneven division of unregulated clustering data, the result is better than the regular criterion method .
(3) Due to complexity and variety of clouds presentation, it is necessary to distinguish and classify clouds' characteristics within a year or inter-seasons. On the basis of each advantage of SOMF (i.e. Self-Organized Mapping Reflection) and PNN (i.e .Probability Neural Network), an improving method of two times classification of clouds is introduced in combination of SOMF and PNN.. The method makes process of cloud classification into two stages: first, it is clustered to the base of samples characteristics without supervision by the mean of SOMF, then, models of clouds classification with their own PNN are build after kinds of classified samples have been trained under supervision and objects' rectification. The method can both consider differentiae of clouds in different seasons and simplify process of cloud classification .Therefore, it can effectively reduce the sampling errors, its classification results conform to reality .
(4) It is hard to distinguish attribute of clouds in transitional area. On the basis of IR-VS two dimension gray space projection of the samples of cloud classification , the characteristic area of the samples is rectified and optimized by the means of Fuzzy C Mean clustering method(i.e. FCM). An improving method that characteristic mean values of the samples replace with random initial centre values in FCM can both avoid defection of that FCM is too sensitive to the initial values and correct the clustering results' distortion to characteristic structure of the samples. Therefore, it can effectively reduce the sampling error and keep basic characteristic structure of cloud samples .
(5) In actual atmosphere, there are some of "fuzzy" clouds which are hard to tell what kinds of clouds are. FCM is an advantage non-supervised clustering arithmetic. It can fairly good realize non-linear distinguishing of both high dimension complicated data and non-determinative cloud patterns with calculating and comparing what the subordination is each pixel of cloud image apart from every clustering center. Nevertheless, the normal FCM has three native defections : less capability of global optimization; clustering results dependent on the initial clustering center which produces in random way; the clustering numbers must be given manually. To counter the defections of FCM, a new comprehensive method that Genetic Arithmetic is used for global optimization and FCM is used for local optimization and FSC (Fuzzy Subtract Clustering) is used for objectively estimating clustering numbers as well is introduced on distinguishing satellite cloud patterns. The test results show that the comprehensive method of cloud classification is obvious advantage over any one of three methods and effectively remedy the defection of FCM and GA on cloud classification, and can be used to practice.
In part two, it is researched on short time forecasting of cloud motion of satellite cloud image. The major content includes:
(1) An arithmetic of cloud motion vectors—local wavelet quadrature is selected to objectively and effectively reflect characteristics of cloud motion . Firstly, the basic thought of computing the cloud motion vectors was given and the four important match methods of Fourier Phase, Cross Correlation, Local Entropy were introduced, analyzed . Then, the four schemes are designed according to the unsteady property of could motion during process of heavily changes and rotations . By evaluation of advantage and disadvantage of each scheme, and comparison and simulation tests, it is found that the local wavelet quadrature is more reasonable to reflect variation and characteristics of clouds . Moreover, an improved method of quality control and cloud motion vectors using for corrections scheme is presented.
(2) A model of the short-time forecast is established based on linear extrapolation of cloud motion vectors. Then, quality control is implemented to computed cloud motion vectors with multi-smoothing successive corrections scheme (i.e. Cressman). After smoothing process, 1-3 hours forecast of cloud motion is tested with Backward Trajectory method. The test results show that the method can effectively decrease deviation of distinguishing cloud motion by range estimation and present unsteady characteristics of could rotating to some extent, and as well as provide forecasting of cloud motion in a more objective and quantity way.
(3) A nonlinear idea of cloud prediction combined EOF decomposition with dynamical system restructure was brought up. Series of the samples of the satellite cloud image were made space-time decomposition by EOF. On basis of relative stability of space in EOF decomposition, Genetic algorithms were introduced to go on dynamical system restructure and parametric inversion of model by EOF time factors, and a nonlinear dynamical model forecasting cloud evolution was established. By means of prediction of EOF time factors mingled with restructure of space-structure modalities, a middle and long-time prediction on unsteady characteristics of cloud motion has been realized. An innovative idea and useful way was created for cloud motion prediction.
本文研究内容大致分两部分。第一部分为多光谱卫星云图的云分类判别研究,主要内容包括:
(1)资料收集整理、误差订正和样本采集。对收集的GMS-5静止气象卫星的红外1、红外2、可见光和水汽四个通道的云图资料进行了灰度与分辨率的标准化转换和太阳高度角订正;从中采集多种典型云类和陆地、水体的若干样本,建立了云类样本库,为随后的云分类研究提供建模和实验的数据信息。
(2)季节内的云分类判别。季节内云的特征识别与分类是日常气象保障的基本内容,针对当前云分类研究中广泛采用的特征空间映射分类法调控性差、云类样本要求高、噪声误差需要人工判断消除等缺点以及常规人工神经网络分类模型对云图梯度和纹理特征描述不足等问题,通过计算云图样本灰度-梯度共生矩阵及其若干特征统计量,用BP神经网络建立了云分类模型。该模型可综合利用云图样本的多种特征信息(如灰度、梯度和纹理),在特征量输入与分类输出间建立起良好的非线性映射关系;通过在网络训练中引入模糊化处理方法,减小了网络复杂程度、加速了网络收敛,分类结果更为准确、合理。所建BP网络分类器模型能够对云-地之间、云-水之间和季节内的多种典型云类进行有效的分类判别。
(3)年际内的云分类判别。在实际业务应用中,需要考虑全年各个季节的云分类问题,即年际尺度和季节间的云分类判别。由于云生成和维持的复杂性和多样性,不同季节的云类主体的形态特征存在较大差异,因此笼统采用全年资料序列建模的模型训练难度大、误差收敛慢、泛化性能差,尤其对季节转换期间的云分类判别效果更差。针对上述问题,本文基于自组织特征映射网络(SOMF)和概率神经网络(PNN)各自优势,提出SOMF网络与PNN网络结合的二级分离云分类方法。该方法将云分类过程分为两个阶段:首先用自组织特征映射网络对样本特征库进行无监督聚类(一级分类);其后再针对分离出的各类样本进行有监督训练和目标修正,分别建立其各自的概率神经网络的云分类模型(二级分类)。上述处理方法不是机械地按照季节划分,而是遵循云类样本特征空间结构的自然相似性进行客观合理的分类建模,既考虑了不同季节云类主体的差异,又简化了云分类过程复杂性,使得季节过渡和转换期间的云分类模型建立和选择有了客观依据,云分类准确率得到了有效提高。
(4)云类过渡区的多属性判别。针对各云类过渡区间存在的多属性(同时具有多种云类特征)问题和云类样本中的误差问题,基于云类样本的红外-可见光二维灰度特征空间,提出了用模糊C均值聚类(FCM)方法调整优化云类样本特征映射集的技术途径。该方法以隶属度为度量,较为合理地描述了云图空间点(尤其是云类过渡区空间点)的多云类属性和云类特征的“模糊”性,其“柔性”分类结果更贴近客观实际和人们的视觉理解,同时既有效地降低了采样误差,又保持了云类样本的基本结构特征。针对常规FCM方法对聚类初值的敏感性,提出用样本特征均值替代常规FCM方法中随机的初始中心的改进办法,改善了常规模糊C均值聚类方法难以检测不规则云结构特征子集的不足。
(5)非典型云类和多层云系的分类判别。实际天气中,除了特征较为明显的“典型”云类外,还存在大量特征较为“模糊”的云系(如天气系统生消演变期间和多种天气系统相互共存等情况下的云类)。针对实际天气中上述非典型云类和多层云系的“模糊性”分类问题,以及常规模糊聚类方法对初始聚类中心选取的随机性和聚类结果的“局部优化”等问题,本文提出遗传算法(GA)全局寻优与模糊C均值聚类(FCM)局部寻优以及模糊减法聚类(FSC)客观估算聚类数等优势互补的研究思想和技术途径。首先用FSC方法客观确定云图中的云类数,其后基于估算的云类数,采用GA进行全局搜索,确定初始聚类中心,最后用FCM方法对GA提供的聚类结果再进行局部的调整优化。试验结果表明,综合云分类方法(FSC-GA-FCM)的分类效果明显优于单一的FCM或GA算法,表现出较好的创新特色和技术优势,并被有效应用于相关科研项目的云分类算法设计之中。
第二部分为卫星云图中云团移动的短时预测研究,主要内容包括:
(1)确定了基于局部小波矩方法的云团特征匹配和移动矢量计算方案。首先系统阐述了云团移动矢量计算的基本思想,重点引入和分析了图像处理中四种重要的匹配方法:傅立叶位相法、交叉相关系数法、局部信息熵方法和局部小波矩方法在云团特征匹配中的应用,并针对云团移动过程中强度变化和旋转等非平稳运动特点,设计了上述四种云团匹配计算方案,通过仿真模拟,分析、评估了各种算法的优缺点,发现局部小波矩方法能够更为合理恰当地表现云团环境的变化和特征匹配,进而为云团预测的运动矢量计算提供了可靠的计算方案。此外,还提出了移动矢量平滑处理和质量控制的改进方法。
(2)建立了基于云团移动矢量线性外推的云团短时预测模型。基于上述优选的云团运动矢量算法和Cressman多次连续平滑方法质量控制,结合后向轨迹方法,设计了云图1～3小时的短时预测模型。试验结果表明,该方法的云图1-3小时的预测结果可有效改善主观目测卫星云图判断云团移动的偏差,并可在一定程度上表现云的旋转等非平稳运动特性,进而为云团移动预测提供了一个客观、定量的技术手段。
(3)提出了经验正交函数分解与动力系统重构相结合的云图非线性预测思想。基于经验正交函数(EOF)方法对卫星云图的样本序列进行时、空分解;在EOF分解的空间场相对稳定的基础上,引入遗传算法对EOF分解的时间系数序列进行动力系统重构和模型参数反演,建立了云团演变的非线性动力预报模型。通过对EOF时间系数预测结果与空间结构模态的重构合成,实现了云图非平稳运动的中、长时效预测,为云图预测提供了一种创新思想方法和实用技术途径。
To compete with hardness of military meteorological service , it is forced to produce lots of advanced theories and practical technologies which can serve the purpose of weather forecast. A satellite cloud image contains a lot of weather information. It is hard and important task to distinguish the characteristics and short-time movement forecast of satellite cloud image timely、correctly、objectively and automatically. To meet the challenge of particularity and complexity of cloud classification and short-time movement forecast of satellite cloud image, it is studied to improve used theory and find a new arithmetic which can be easily put into use based on idea of crossing of subjects and methods in this paper.
The paper contains two parts. In part one, it is mainly aimed at distinguishing and classification of satellite cloud image. There are five subjects:
(1) After standard conversion of gray and recognizing ratio and as well as rectifying to the height angle of the Sun from satellite cloud image of GMS-5 through four bands of IRK IR2、VS and WV , it is built of a sample base which collects numbers of samples of land and water and seven types of typical clouds as well, which is used for study of cloud classification;
(2) Distinguishing and classification of cloud characteristics inner season is basic part of ordinary weather services. On the basis of above works, the samples of cloud classification are reflected to two dimension gray space of IR and VS by the means of Characteristic Space Projection (i.e. CSP). It is found the criteria of classification of cloud pixel with determination of falling area of the samples. The test results show that the method of CSP is simple and easy, especially suited for uneven division of unregulated clustering data, the result is better than the regular criterion method .
(3) Due to complexity and variety of clouds presentation, it is necessary to distinguish and classify clouds' characteristics within a year or inter-seasons. On the basis of each advantage of SOMF (i.e. Self-Organized Mapping Reflection) and PNN (i.e .Probability Neural Network), an improving method of two times classification of clouds is introduced in combination of SOMF and PNN.. The method makes process of cloud classification into two stages: first, it is clustered to the base of samples characteristics without supervision by the mean of SOMF, then, models of clouds classification with their own PNN are build after kinds of classified samples have been trained under supervision and objects' rectification. The method can both consider differentiae of clouds in different seasons and simplify process of cloud classification .Therefore, it can effectively reduce the sampling errors, its classification results conform to reality .
(4) It is hard to distinguish attribute of clouds in transitional area. On the basis of IR-VS two dimension gray space projection of the samples of cloud classification , the characteristic area of the samples is rectified and optimized by the means of Fuzzy C Mean clustering method(i.e. FCM). An improving method that characteristic mean values of the samples replace with random initial centre values in FCM can both avoid defection of that FCM is too sensitive to the initial values and correct the clustering results' distortion to characteristic structure of the samples. Therefore, it can effectively reduce the sampling error and keep basic characteristic structure of cloud samples .
(5) In actual atmosphere, there are some of "fuzzy" clouds which are hard to tell what kinds of clouds are. FCM is an advantage non-supervised clustering arithmetic. It can fairly good realize non-linear distinguishing of both high dimension complicated data and non-determinative cloud patterns with calculating and comparing what the subordination is each pixel of cloud image apart from every clustering center. Nevertheless, the normal FCM has three native defections : less capability of global optimization; clustering results dependent on the initial clustering center which produces in random way; the clustering numbers must be given manually. To counter the defections of FCM, a new comprehensive method that Genetic Arithmetic is used for global optimization and FCM is used for local optimization and FSC (Fuzzy Subtract Clustering) is used for objectively estimating clustering numbers as well is introduced on distinguishing satellite cloud patterns. The test results show that the comprehensive method of cloud classification is obvious advantage over any one of three methods and effectively remedy the defection of FCM and GA on cloud classification, and can be used to practice.
In part two, it is researched on short time forecasting of cloud motion of satellite cloud image. The major content includes:
(1) An arithmetic of cloud motion vectors—local wavelet quadrature is selected to objectively and effectively reflect characteristics of cloud motion . Firstly, the basic thought of computing the cloud motion vectors was given and the four important match methods of Fourier Phase, Cross Correlation, Local Entropy were introduced, analyzed . Then, the four schemes are designed according to the unsteady property of could motion during process of heavily changes and rotations . By evaluation of advantage and disadvantage of each scheme, and comparison and simulation tests, it is found that the local wavelet quadrature is more reasonable to reflect variation and characteristics of clouds . Moreover, an improved method of quality control and cloud motion vectors using for corrections scheme is presented.
(2) A model of the short-time forecast is established based on linear extrapolation of cloud motion vectors. Then, quality control is implemented to computed cloud motion vectors with multi-smoothing successive corrections scheme (i.e. Cressman). After smoothing process, 1-3 hours forecast of cloud motion is tested with Backward Trajectory method. The test results show that the method can effectively decrease deviation of distinguishing cloud motion by range estimation and present unsteady characteristics of could rotating to some extent, and as well as provide forecasting of cloud motion in a more objective and quantity way.
(3) A nonlinear idea of cloud prediction combined EOF decomposition with dynamical system restructure was brought up. Series of the samples of the satellite cloud image were made space-time decomposition by EOF. On basis of relative stability of space in EOF decomposition, Genetic algorithms were introduced to go on dynamical system restructure and parametric inversion of model by EOF time factors, and a nonlinear dynamical model forecasting cloud evolution was established. By means of prediction of EOF time factors mingled with restructure of space-structure modalities, a middle and long-time prediction on unsteady characteristics of cloud motion has been realized. An innovative idea and useful way was created for cloud motion prediction.
引文
[01] Arking,A.,1964,Latitude distribution of cloud cover from Tiros -3 photographs.Science, 143,569-572.
[02] Miller,D.B.,and R.GFeddes,1971,Global atlas of relative cloud cover, 1967-1970.United States Air Force/Dept.of Commerce. Washington, D C,237pp.
[03] Koffler,R,A.G. Decotiis, and P.K.Rao, 1973,A procedure for estimating cloud amount and height from satellite infrared radiation data.Mon.Wea.Rev, 101, 240-243
[04] Minnis ,P. and E.F.Harrison.1984, Diurnal variability of regional cloud and clear-sky radiative parameters derived from GOES data .Part-1 Analysis method ;Part-2:November 1978 cloud distribution;Part-3 :November 1978 radiative parameters.J.Clim. Appl.Meteor.,23,993-1051.
[05] Saunders ,R.W.,K.T.Kriebel,1988,An improved method for detecting clear sky and cloudy radiances from SVHRR data .Int.J.Remote Sensing,9,1,123-150.
[06] Puri ,K.,and N.E.Davidson,1992,The use of infared satellite cloud imagery data as proxy data for moisture and diabatic heating in data assimilation. Mon.Wea.Rev., 120,2329-2341.
[07] Allen,Robert C.,JR.:Automated satellite cloud analysis:a multispectral approach to the problem of snow/cloud discrimination.M.S.thesis,U.S.Naval Postgraduate School,Monterey. 111 pp, 1987.
[08] Allen,R.C.,JR.,P.A.Durkee,and C.H.Wash:Snow/Cloud discrimination with multispectral satellite measurements.J.Appl.Meteor.,29,994-1004,1990.
[09] Arking,A.,and J.D.Childs:Retrieval of cloud cover parameters from multispectral satellite images. J.Climate Appl.Meteor.,24,322-333,1985.
[10] Girolamo,L.D.,A comparison of 15 global,non-parameteric,automated threshold selection procedures for cloud detection. Ninth Conference on Satellite Meteorology and Oceanography, 197-200, 1998.
[11] Garand,L.C:Automated recognition of oceanic cloud patterns. Part I:methodology and application to cloud climatology.J.Climate., 1,20-39, 1988.
[12] Shenk,R.T.Holub and R.A.Neff:A multispectral cloud type identification method developed for tropical ocean area with Nimbus-3 MRIR measurements. Mon.Wea. Rev,284-291,1976.
[13] Szejwach,G:Determination of semi-transparent cirrus cloud temperature from infrared radiances. Application to METEOSAT. J.Appl.Meteoro., 21, 384-393, 1982.
[14] Coakley ,J.A.,and Breatherton,F.P., 1982,Cloud cover from high resolution scanner data:Decting and allowing fior partially filled fields of view.J.Geophys.Res.,87,4917-4923.
[15] Desbois, M.,G.Seze and G.Szejwach,1982 Automatic classification of clouds on METEOSAT imagery application to high-level clouds.J.Appl.Meteor, 21, 401-402.
[16] Schiffer,K.,and W.B.Rossow:The International Satellite Cloud Climatology Project:the first project of the World Climate Research Programme. Bull.Amer.Soc,Vol.64,779-784,1983.
[17]Weare,B.C.,Combined satellite- and surface-based obserbations of coluds,Ninth Conference on Satellite Meteorology and Oceanography,212-123,1998.
[18]Bezdek J C.A convergence theorem for the fuzzy ISODATA clustering algorithms,IEEE Tran.,1980,PAMI-2:I-7
[19]Jonathan Lee,Ronald c.:A neural network approach to classification,IEEE Transation on geoscience and Remote Sensing.Vol.28,No.5,846-855,1990.
[20]Peak,J.E,Tag P M.:Segmentation of satellite imagery using hierarchical thresholding and neural networks.J.Appl.Meteor,33,605-616,1994.
[21]Alexander,J.,E.M.Corwin,D.11oyd,A.M.Logar,and Welch,Cloud classificatio in polar and desert regions and smoke classification from biomass burning using a hierarchical neural network,Eight Conference on Satellite meteorology and Ocernography,303-307,1996.
[22]Welch,R.W.,V.Tovinkere,J.Titlow and B.A.Baum,Global single and multiple cloud classification with a fuzzy logic expert system.Eighth Conference on Satellite Meteorology and Oceanography,347-350,1996.
[23]Miller,S.W.,and W.J.Emery:An automated neural network cloud classifier for use over land and ocean surfaces.J.Appl.Meteoro.,36,1346-1362,1997.
[24]Baum et al.:Automatic cloud classification of global AVHRR data using a fuzzy logic approach.J.Appl.Meteor,Vol.36,1519-1535,1997.
[25]秦其明,陆荣建.分形与神经网络方法在卫星数字图像分类中的应用.北京大学学报(自然科学版)36(6):857-864,2000.
[26]梁益同,胡江林.NOAA卫星图像神经网络分类方法的探讨.武汉测绘科技大学学报,25(2):148-152,2000.
[27]Lee,J.,R.C.Weger,S.K.Sengupta,and R.M.Welch:A neural network approach to cloud classification.IEEE Trans.Geosci.Remote Sens.,28,846-855,1990.
[28]Welch,R.M.,S.K.sengupta,and A.K.Goroch:Polar cloud and surface classification using AVHRR imagery:An intercomparison of methods.J.Appl.Meteoro.,31,05-420,1992.
[29]Bankert,R.L.:Cloud classification of AVHRR imagery in maritime regions using a probabilistic neural network.J.Appl.Meteor.,33,909-918,1994.
[30]Key,J.,J.A.Maslanik,and A.J.Schweiger,Classification of merged AVHRR and SMMR arctic data with neural nerworks,Photogram Eng.Remote Sens.,55,1331 - 1338,1989.
[31]方宗义,刘玉洁,朱小祥.卫星云参数处理方法和1991年的云气候特征分析.应用气象学报,5(2):135-142,1994.
[32]周伟,李万彪.用GMS-5红外资料进行云的分类识别.北京大学学报,39(1):83-90,2003.
[33]刘健庄.基于二维直方图的图象模糊聚类分割法.模糊技术与应用选编(1).北京:北京航空航天大学出版社,615-621,1998.
[34]师春香,吴蓉璋,项续康.多阈值和神经网络卫星云图云系自动分割试验.应用气象学报,12(1):70-78,2001.
[35]师春香,翟建华.用神经网络方法对NOAA-AVHRR资料进行云客观分类.气象学报,60(2):25 1-255,2002.
[36]李俊,周凤仙.气象卫星台风云图的自动识别方法及其应用.应用气象学报,3(4):402-409,1992.
[37]余波等.模糊神经网络在台风云系图像识别中的应用.气象,22(1),1998.
[38]何明元,石汉青.静止气象卫星资料处理.解放军理工大学学报,3(1):75-80,2002.
[39]M.J.巴德,G.S.福布斯,J.R.戈兰特等编,卢乃锰,刘健译.卫星与雷达图像在天气预报中的应用.北京:科学出版社,1998.
[40]沈清,胡德文,时春.神经网络应用技术.长沙:国防科技大学出版社,1993.
[41]夏德深,金盛,王健.基于分数维与灰度梯度共生矩阵的气象云图识别(Ⅰ)-分数维对纹理复杂度和粗糙度的描述.23(3),1999.
[42]夏德深,金盛,王健.基于分数维与灰度梯度共生矩阵的气象云图识别(Ⅱ)-灰度梯度共生矩阵对纹理统计特征的描述.南京理工大学学报,23(4),1999.
[43]白慧卿,方宗义,吴蓉璋等.基于人工神经网络的GMS云图四类云系的识别.应用气象学报,9(4),402-409,1998.
[44]Gu,Z.Q.,C.N.Duncan.R.E.Renshaw,M.A.Muggleestone,C.F.N.Cowan,and RM.Grant:Comparison of techniques for measuring cloud texture in remotely sensed satellite meteorological image data.IEEE Proc.F:Radar Signal Processing,136,236-248,1989.
[45]Gu,Z.Q.,C.N.Duncan.R.E.Renshaw,M.A.Muggleestone,C.F.N.Cowan,and P.M.Grant:Textural and spectral features as an aid to cloud classification.Int.J.Remote Sens.,12,953-968,1991.
[46]Haralick,R.M.,K.Shanmugam,and I.Dinstein:Textural features for image classification.IEEE Trans.Syst.Man Cybern.,3,610-621,1973.
[47]Key,J.:Cloud cover analysis with Arctic Aadvanced Very High Resolution radiometer data 2.Classification with spectral and textural measures,J.Geophys.Res.,95,7661-7675,1990.
[48]Kuo,K-S.,R.M.Welch and S.K.Sengupta,Structural and textural characteristics of cirrus clouds observed using high spatial resolution LANDSAT magery,J.Appl.Meteoro,27,1242-1260.
[49]Welch,R.M.,S.K.Sengupta,and D.W.Chen:Cloud field classification based upon high spatial resolution textural features,Part Ⅰ,Gray level cooccurrence matrix approach.J.Geophys.Res.,93,12,663-12,1988.
[50]Welch,R.M.,M.S.Navar,and S.K.Sengupta:The effect of spatial resolution upon texture-based cloud field classifications.J.Geophys.Res.,94,No.D12,14,767-14,781,1989.
[51]Peleg J,Nato R,Harley R,et al.Multiple resolution textures analysis and classification.IEEE TRANS ON PAMI,1985,6(4):518-523
[52]Chen,D.W.,S.K.Sengupta,and R.M.Welch,Cloud field classification based upon high spatial resolution textural features,Part 2,Simplified vector approaches,J.Geophys.Res.,94,14,749-14,765,1989.
[53]Keller J,Crownover R,Chen R.characteristics of nature scenes related to the fractal.dimension.IEEE TRANS ON PAMI,1987,9(5):621-627
[54]Sarkar N,Chaudhuri.An efficient approach to estimate fractal dimension of texture images.Pattern Recognition.1992,25(9):1035-1041
[55]Martin T.Hagan,Howard B.Demuth,戴葵等译.神经网络设计.北京:机械工业出版社,2002.
[56]阎平凡,张长水.人工神经网络与模拟进化计算.北京:清华大学出版社,2000.
[57]高新波,谢维信.模糊聚类理论发展及应用的研究进展.科学通报,44(21),1999.
[58]何清.模糊聚类分析理论与应用研究进展.模糊系统与数学,12(2),1998.
[59]高新波.模糊聚类分析及其应用.西安:西安电子科技大学出版社,2004.
[60]郑宏.遗传算法在影像处理与分析中的应用.北京:测绘出版社,2003.
[61]王晓平,曹立明.遗传算法.理论.应用与软件实现.西安:西安交通大学出版社,2002.
[62]玄光男,程润伟.遗传算法与工程优化.北京:清华大学出版社,2004.
[63]吴晓莉,林哲辉.MATLAB辅助模糊系统设计.西安:西安电子科技大学,2002.
[64]Fujita T,Bradbnry D L,Murino C,etal.A study of mesoscale cloud motions computed from ATS-I and terrestrial photographs,SMRP Res.Pap.71,University of Chicago.1968,25.
[65]Smith E A.The MclDAS system.IEEE Trans.Geosci Electron.CE-13.1975,123-136.
[66]Endlich,R.M.,D.E.Wolf,D.J.Hall and A.E.Brain,1971:Use of a pattern recognition technique for determining cloud motions from sequences of satellite photographs.Appl.Meteor.,10,104-117.
[67]Arking A,Robert CL,Rosenfield A.A fourrier approach to cloud motion estimation.J.Appl.Meteor.,17:735-744,1978.
[68]白洁,王洪庆,陶祖钰.GMS卫星红外云图强对流云团的识别与追踪.热带气象学报.13(2):158-167,1997.
[69]龚克,叶大鲁等.卫星云图预测的卫星矢量方法.中国图形图像学报.Vol.5,2000,349-352.
[70]缪绍纲.数字图像处理-活用Matlab.成都:西南交通大学出版社,2001.
[71]白洁,王洪庆,陶祖钰.GMS卫星红外云图云迹风的反演.北京大学学报.33(1):85-92,1997.
[72]李冬霞,曾禹村.基于速度特征矢量提取运动目标的图像分割方法.北京理工大学学报.20(3):347-351,2000.
[73]方兆宝,林珲,吴立辛.流行群运动目标自动识别与跟踪技术研究.遥感学报,8(1):14-21,2004.
[74]Baucer,K.G.,1976:A comparison of cloud motion winds with coinciding radiosonde winds.Mon.Wea.Rev.,104,922-931.
[75]徐飞,施晓红.MATLAB应用图像处理.西安:西安电子科技大学出版社,2002.
[76]张兆礼,赵春晖,梅晓丹.现代图像处理技术及Matlab实现.北京:人民邮电出版社,2001.
[77]王振会,许建明,G.Kell.基于傅立叶相位分析的卫星云图导风技术.气象科学.24(1),8-14,2004.
[78]Robert C.Lo and Azriel Rosenfeld.A Fourier Approach to Cloud Motion Estimation.J.Appl.Meteor.Vol.17,735-744,1978.
[79]Ball,C.H.,A.E.Brain,G.H.Burch and D.J.Hall,1964:Graphical data processing research study and experimental investigation,research study and experimental investigation.Rept.16,Contract.
[80]Billingsley,J.B.,1976:Interractive image processing for meteorological applications at NASA/Goddard Space Flight Center.Preprints Seventh Conj.Acrospace and Aeronautical Meteorology,Melbourne,Amer.Meteor.Soc.,268-275.
[81]Thosmas M.H.and N.Thomas.A short-term cloud forecast scheme using cross-correlations.Weather and Forecasting.Vol.8.No.4,1993.8:401-411.
[82]Li Zhen jun(1998) Estimation of Cloud Motion Using Cross-Correlation.Advances in Atmospheric Sciences 15:277-282.
[83]Leese.J.A.and C.S.Ovak.An automated technique for obtaining cloud motion from geosynchronous satellite data using cross-correlation.J.Appl.Meteor,1971.
[84]Weinstein,F.S.,1972:An advantageous method of performing cross-correlational analysis.Proc.IEEE,60,449-450.
[85]C.S.Novak and B.B.Clark,1971:An automated technique for botaining coud motion from geosynchronous satellite data using cross correlation.J.Appl.Meteor.,10,118-132.
[86]Briggs,B.H.,1968:On the analysis of moving patterns in geophysics-I.Correlation analysis J.Atmos.Terr.Phys.30,1777-1788.Marayland.
[87]田金文,苏康,柳健.基于局部熵差的图像匹配方法-算法及计算机仿真.宇航学报.No.1,1999.
[88]柳健等.基于局部熵差的快速图像匹配方法.华中理工大学研究报告,1995.
[89]潘秀琴,催克宁等.基于局部小波矩的图像匹配算法.计算机工程与应用.2002.11,18-20
[90]严柏军,郑键,王克勇.基于不变矩特征匹配的快速目标检测算法.红外技术.23(6),2001,8-12.
[91]张国柱,王程,王润生.基于小波变换的多分辨率图像匹配方法.计算机工程与应用.13,113-114,2002.
[92]初秀琴,李玉山,徐善锋.一种快速分类搜索运动估计方法.中国图像图形学报.
[93]章毓晋.图像分割.北京:科学出版社,2001.
[94]Chan,Y.M.,J.J.Tecson,and T.T.Fujita,1973:METRACOM system of cloud veloctity determination from geostationary satellite pictures.SMPP Res.Pap.110,University of Chicargo.
[95]Fujita,T.,D.L.Bradbury;C.Murino and L.HulI,1968:A study of mesoscale cloud motions computed from ATS-I and terrestrial photographs.SMRP Res.Pap.71,University of Chicago,25 pp.
[96]Hubert,L.F.Whitney,Jr.,1971:Wind estimation from geostationary satellite pictures.Mon.Wea.Rev.99,665-672.
[97]Leese,J.A.,and E.S.Epstein,1963:Application of two dimensional spectral analysis to the quantification of satellite cloud photographs.J.Appl.Meteor.,2,629-644.
[98]Takens F.Detecting strange attractors in fluid turbulence[J].Lecture Notes in Mathematics,898(2):361-381,1981.
[99]Chamey,J,G.,and J.G.,De Voro.Multiple flow equilibria in the atmosphere and blocking.J.Atmos.Sci.,36.1205-1216,1979.
[100]缪锦海,丁敏芳.热力强迫作用下大气平衡态的季节变化和突变.副高北跳[J].中国科学(B),1:87-96,1985.
[101]柳崇键,陶诗言.副热带高压北跳与月尖突变[J].中国科学(B),5:474-480,1983.
[102]李杰友,熊学农,刘秀玉.基于EOF迭代的月径流长期预报[J].河海大学学报,29(2):3,2001.
[103]李跃清.相空间EOF方法及其在气候诊断中的应用[J].高原气象,20(1):2,2001.
[104]田纪伟,孙孚.相空间反演方法及其在海洋资料分析中的应用[J].海洋学报,18(4):1-10,1996.
[105]黄建平,衣育红.利用观测资料反演非线性动力模型[J].中国科学,3(3):331-336,1991.
[106]王凌.智能优化算法及其应用[M].北京:清华大学出版社,2001.
[02] Miller,D.B.,and R.GFeddes,1971,Global atlas of relative cloud cover, 1967-1970.United States Air Force/Dept.of Commerce. Washington, D C,237pp.
[03] Koffler,R,A.G. Decotiis, and P.K.Rao, 1973,A procedure for estimating cloud amount and height from satellite infrared radiation data.Mon.Wea.Rev, 101, 240-243
[04] Minnis ,P. and E.F.Harrison.1984, Diurnal variability of regional cloud and clear-sky radiative parameters derived from GOES data .Part-1 Analysis method ;Part-2:November 1978 cloud distribution;Part-3 :November 1978 radiative parameters.J.Clim. Appl.Meteor.,23,993-1051.
[05] Saunders ,R.W.,K.T.Kriebel,1988,An improved method for detecting clear sky and cloudy radiances from SVHRR data .Int.J.Remote Sensing,9,1,123-150.
[06] Puri ,K.,and N.E.Davidson,1992,The use of infared satellite cloud imagery data as proxy data for moisture and diabatic heating in data assimilation. Mon.Wea.Rev., 120,2329-2341.
[07] Allen,Robert C.,JR.:Automated satellite cloud analysis:a multispectral approach to the problem of snow/cloud discrimination.M.S.thesis,U.S.Naval Postgraduate School,Monterey. 111 pp, 1987.
[08] Allen,R.C.,JR.,P.A.Durkee,and C.H.Wash:Snow/Cloud discrimination with multispectral satellite measurements.J.Appl.Meteor.,29,994-1004,1990.
[09] Arking,A.,and J.D.Childs:Retrieval of cloud cover parameters from multispectral satellite images. J.Climate Appl.Meteor.,24,322-333,1985.
[10] Girolamo,L.D.,A comparison of 15 global,non-parameteric,automated threshold selection procedures for cloud detection. Ninth Conference on Satellite Meteorology and Oceanography, 197-200, 1998.
[11] Garand,L.C:Automated recognition of oceanic cloud patterns. Part I:methodology and application to cloud climatology.J.Climate., 1,20-39, 1988.
[12] Shenk,R.T.Holub and R.A.Neff:A multispectral cloud type identification method developed for tropical ocean area with Nimbus-3 MRIR measurements. Mon.Wea. Rev,284-291,1976.
[13] Szejwach,G:Determination of semi-transparent cirrus cloud temperature from infrared radiances. Application to METEOSAT. J.Appl.Meteoro., 21, 384-393, 1982.
[14] Coakley ,J.A.,and Breatherton,F.P., 1982,Cloud cover from high resolution scanner data:Decting and allowing fior partially filled fields of view.J.Geophys.Res.,87,4917-4923.
[15] Desbois, M.,G.Seze and G.Szejwach,1982 Automatic classification of clouds on METEOSAT imagery application to high-level clouds.J.Appl.Meteor, 21, 401-402.
[16] Schiffer,K.,and W.B.Rossow:The International Satellite Cloud Climatology Project:the first project of the World Climate Research Programme. Bull.Amer.Soc,Vol.64,779-784,1983.
[17]Weare,B.C.,Combined satellite- and surface-based obserbations of coluds,Ninth Conference on Satellite Meteorology and Oceanography,212-123,1998.
[18]Bezdek J C.A convergence theorem for the fuzzy ISODATA clustering algorithms,IEEE Tran.,1980,PAMI-2:I-7
[19]Jonathan Lee,Ronald c.:A neural network approach to classification,IEEE Transation on geoscience and Remote Sensing.Vol.28,No.5,846-855,1990.
[20]Peak,J.E,Tag P M.:Segmentation of satellite imagery using hierarchical thresholding and neural networks.J.Appl.Meteor,33,605-616,1994.
[21]Alexander,J.,E.M.Corwin,D.11oyd,A.M.Logar,and Welch,Cloud classificatio in polar and desert regions and smoke classification from biomass burning using a hierarchical neural network,Eight Conference on Satellite meteorology and Ocernography,303-307,1996.
[22]Welch,R.W.,V.Tovinkere,J.Titlow and B.A.Baum,Global single and multiple cloud classification with a fuzzy logic expert system.Eighth Conference on Satellite Meteorology and Oceanography,347-350,1996.
[23]Miller,S.W.,and W.J.Emery:An automated neural network cloud classifier for use over land and ocean surfaces.J.Appl.Meteoro.,36,1346-1362,1997.
[24]Baum et al.:Automatic cloud classification of global AVHRR data using a fuzzy logic approach.J.Appl.Meteor,Vol.36,1519-1535,1997.
[25]秦其明,陆荣建.分形与神经网络方法在卫星数字图像分类中的应用.北京大学学报(自然科学版)36(6):857-864,2000.
[26]梁益同,胡江林.NOAA卫星图像神经网络分类方法的探讨.武汉测绘科技大学学报,25(2):148-152,2000.
[27]Lee,J.,R.C.Weger,S.K.Sengupta,and R.M.Welch:A neural network approach to cloud classification.IEEE Trans.Geosci.Remote Sens.,28,846-855,1990.
[28]Welch,R.M.,S.K.sengupta,and A.K.Goroch:Polar cloud and surface classification using AVHRR imagery:An intercomparison of methods.J.Appl.Meteoro.,31,05-420,1992.
[29]Bankert,R.L.:Cloud classification of AVHRR imagery in maritime regions using a probabilistic neural network.J.Appl.Meteor.,33,909-918,1994.
[30]Key,J.,J.A.Maslanik,and A.J.Schweiger,Classification of merged AVHRR and SMMR arctic data with neural nerworks,Photogram Eng.Remote Sens.,55,1331 - 1338,1989.
[31]方宗义,刘玉洁,朱小祥.卫星云参数处理方法和1991年的云气候特征分析.应用气象学报,5(2):135-142,1994.
[32]周伟,李万彪.用GMS-5红外资料进行云的分类识别.北京大学学报,39(1):83-90,2003.
[33]刘健庄.基于二维直方图的图象模糊聚类分割法.模糊技术与应用选编(1).北京:北京航空航天大学出版社,615-621,1998.
[34]师春香,吴蓉璋,项续康.多阈值和神经网络卫星云图云系自动分割试验.应用气象学报,12(1):70-78,2001.
[35]师春香,翟建华.用神经网络方法对NOAA-AVHRR资料进行云客观分类.气象学报,60(2):25 1-255,2002.
[36]李俊,周凤仙.气象卫星台风云图的自动识别方法及其应用.应用气象学报,3(4):402-409,1992.
[37]余波等.模糊神经网络在台风云系图像识别中的应用.气象,22(1),1998.
[38]何明元,石汉青.静止气象卫星资料处理.解放军理工大学学报,3(1):75-80,2002.
[39]M.J.巴德,G.S.福布斯,J.R.戈兰特等编,卢乃锰,刘健译.卫星与雷达图像在天气预报中的应用.北京:科学出版社,1998.
[40]沈清,胡德文,时春.神经网络应用技术.长沙:国防科技大学出版社,1993.
[41]夏德深,金盛,王健.基于分数维与灰度梯度共生矩阵的气象云图识别(Ⅰ)-分数维对纹理复杂度和粗糙度的描述.23(3),1999.
[42]夏德深,金盛,王健.基于分数维与灰度梯度共生矩阵的气象云图识别(Ⅱ)-灰度梯度共生矩阵对纹理统计特征的描述.南京理工大学学报,23(4),1999.
[43]白慧卿,方宗义,吴蓉璋等.基于人工神经网络的GMS云图四类云系的识别.应用气象学报,9(4),402-409,1998.
[44]Gu,Z.Q.,C.N.Duncan.R.E.Renshaw,M.A.Muggleestone,C.F.N.Cowan,and RM.Grant:Comparison of techniques for measuring cloud texture in remotely sensed satellite meteorological image data.IEEE Proc.F:Radar Signal Processing,136,236-248,1989.
[45]Gu,Z.Q.,C.N.Duncan.R.E.Renshaw,M.A.Muggleestone,C.F.N.Cowan,and P.M.Grant:Textural and spectral features as an aid to cloud classification.Int.J.Remote Sens.,12,953-968,1991.
[46]Haralick,R.M.,K.Shanmugam,and I.Dinstein:Textural features for image classification.IEEE Trans.Syst.Man Cybern.,3,610-621,1973.
[47]Key,J.:Cloud cover analysis with Arctic Aadvanced Very High Resolution radiometer data 2.Classification with spectral and textural measures,J.Geophys.Res.,95,7661-7675,1990.
[48]Kuo,K-S.,R.M.Welch and S.K.Sengupta,Structural and textural characteristics of cirrus clouds observed using high spatial resolution LANDSAT magery,J.Appl.Meteoro,27,1242-1260.
[49]Welch,R.M.,S.K.Sengupta,and D.W.Chen:Cloud field classification based upon high spatial resolution textural features,Part Ⅰ,Gray level cooccurrence matrix approach.J.Geophys.Res.,93,12,663-12,1988.
[50]Welch,R.M.,M.S.Navar,and S.K.Sengupta:The effect of spatial resolution upon texture-based cloud field classifications.J.Geophys.Res.,94,No.D12,14,767-14,781,1989.
[51]Peleg J,Nato R,Harley R,et al.Multiple resolution textures analysis and classification.IEEE TRANS ON PAMI,1985,6(4):518-523
[52]Chen,D.W.,S.K.Sengupta,and R.M.Welch,Cloud field classification based upon high spatial resolution textural features,Part 2,Simplified vector approaches,J.Geophys.Res.,94,14,749-14,765,1989.
[53]Keller J,Crownover R,Chen R.characteristics of nature scenes related to the fractal.dimension.IEEE TRANS ON PAMI,1987,9(5):621-627
[54]Sarkar N,Chaudhuri.An efficient approach to estimate fractal dimension of texture images.Pattern Recognition.1992,25(9):1035-1041
[55]Martin T.Hagan,Howard B.Demuth,戴葵等译.神经网络设计.北京:机械工业出版社,2002.
[56]阎平凡,张长水.人工神经网络与模拟进化计算.北京:清华大学出版社,2000.
[57]高新波,谢维信.模糊聚类理论发展及应用的研究进展.科学通报,44(21),1999.
[58]何清.模糊聚类分析理论与应用研究进展.模糊系统与数学,12(2),1998.
[59]高新波.模糊聚类分析及其应用.西安:西安电子科技大学出版社,2004.
[60]郑宏.遗传算法在影像处理与分析中的应用.北京:测绘出版社,2003.
[61]王晓平,曹立明.遗传算法.理论.应用与软件实现.西安:西安交通大学出版社,2002.
[62]玄光男,程润伟.遗传算法与工程优化.北京:清华大学出版社,2004.
[63]吴晓莉,林哲辉.MATLAB辅助模糊系统设计.西安:西安电子科技大学,2002.
[64]Fujita T,Bradbnry D L,Murino C,etal.A study of mesoscale cloud motions computed from ATS-I and terrestrial photographs,SMRP Res.Pap.71,University of Chicago.1968,25.
[65]Smith E A.The MclDAS system.IEEE Trans.Geosci Electron.CE-13.1975,123-136.
[66]Endlich,R.M.,D.E.Wolf,D.J.Hall and A.E.Brain,1971:Use of a pattern recognition technique for determining cloud motions from sequences of satellite photographs.Appl.Meteor.,10,104-117.
[67]Arking A,Robert CL,Rosenfield A.A fourrier approach to cloud motion estimation.J.Appl.Meteor.,17:735-744,1978.
[68]白洁,王洪庆,陶祖钰.GMS卫星红外云图强对流云团的识别与追踪.热带气象学报.13(2):158-167,1997.
[69]龚克,叶大鲁等.卫星云图预测的卫星矢量方法.中国图形图像学报.Vol.5,2000,349-352.
[70]缪绍纲.数字图像处理-活用Matlab.成都:西南交通大学出版社,2001.
[71]白洁,王洪庆,陶祖钰.GMS卫星红外云图云迹风的反演.北京大学学报.33(1):85-92,1997.
[72]李冬霞,曾禹村.基于速度特征矢量提取运动目标的图像分割方法.北京理工大学学报.20(3):347-351,2000.
[73]方兆宝,林珲,吴立辛.流行群运动目标自动识别与跟踪技术研究.遥感学报,8(1):14-21,2004.
[74]Baucer,K.G.,1976:A comparison of cloud motion winds with coinciding radiosonde winds.Mon.Wea.Rev.,104,922-931.
[75]徐飞,施晓红.MATLAB应用图像处理.西安:西安电子科技大学出版社,2002.
[76]张兆礼,赵春晖,梅晓丹.现代图像处理技术及Matlab实现.北京:人民邮电出版社,2001.
[77]王振会,许建明,G.Kell.基于傅立叶相位分析的卫星云图导风技术.气象科学.24(1),8-14,2004.
[78]Robert C.Lo and Azriel Rosenfeld.A Fourier Approach to Cloud Motion Estimation.J.Appl.Meteor.Vol.17,735-744,1978.
[79]Ball,C.H.,A.E.Brain,G.H.Burch and D.J.Hall,1964:Graphical data processing research study and experimental investigation,research study and experimental investigation.Rept.16,Contract.
[80]Billingsley,J.B.,1976:Interractive image processing for meteorological applications at NASA/Goddard Space Flight Center.Preprints Seventh Conj.Acrospace and Aeronautical Meteorology,Melbourne,Amer.Meteor.Soc.,268-275.
[81]Thosmas M.H.and N.Thomas.A short-term cloud forecast scheme using cross-correlations.Weather and Forecasting.Vol.8.No.4,1993.8:401-411.
[82]Li Zhen jun(1998) Estimation of Cloud Motion Using Cross-Correlation.Advances in Atmospheric Sciences 15:277-282.
[83]Leese.J.A.and C.S.Ovak.An automated technique for obtaining cloud motion from geosynchronous satellite data using cross-correlation.J.Appl.Meteor,1971.
[84]Weinstein,F.S.,1972:An advantageous method of performing cross-correlational analysis.Proc.IEEE,60,449-450.
[85]C.S.Novak and B.B.Clark,1971:An automated technique for botaining coud motion from geosynchronous satellite data using cross correlation.J.Appl.Meteor.,10,118-132.
[86]Briggs,B.H.,1968:On the analysis of moving patterns in geophysics-I.Correlation analysis J.Atmos.Terr.Phys.30,1777-1788.Marayland.
[87]田金文,苏康,柳健.基于局部熵差的图像匹配方法-算法及计算机仿真.宇航学报.No.1,1999.
[88]柳健等.基于局部熵差的快速图像匹配方法.华中理工大学研究报告,1995.
[89]潘秀琴,催克宁等.基于局部小波矩的图像匹配算法.计算机工程与应用.2002.11,18-20
[90]严柏军,郑键,王克勇.基于不变矩特征匹配的快速目标检测算法.红外技术.23(6),2001,8-12.
[91]张国柱,王程,王润生.基于小波变换的多分辨率图像匹配方法.计算机工程与应用.13,113-114,2002.
[92]初秀琴,李玉山,徐善锋.一种快速分类搜索运动估计方法.中国图像图形学报.
[93]章毓晋.图像分割.北京:科学出版社,2001.
[94]Chan,Y.M.,J.J.Tecson,and T.T.Fujita,1973:METRACOM system of cloud veloctity determination from geostationary satellite pictures.SMPP Res.Pap.110,University of Chicargo.
[95]Fujita,T.,D.L.Bradbury;C.Murino and L.HulI,1968:A study of mesoscale cloud motions computed from ATS-I and terrestrial photographs.SMRP Res.Pap.71,University of Chicago,25 pp.
[96]Hubert,L.F.Whitney,Jr.,1971:Wind estimation from geostationary satellite pictures.Mon.Wea.Rev.99,665-672.
[97]Leese,J.A.,and E.S.Epstein,1963:Application of two dimensional spectral analysis to the quantification of satellite cloud photographs.J.Appl.Meteor.,2,629-644.
[98]Takens F.Detecting strange attractors in fluid turbulence[J].Lecture Notes in Mathematics,898(2):361-381,1981.
[99]Chamey,J,G.,and J.G.,De Voro.Multiple flow equilibria in the atmosphere and blocking.J.Atmos.Sci.,36.1205-1216,1979.
[100]缪锦海,丁敏芳.热力强迫作用下大气平衡态的季节变化和突变.副高北跳[J].中国科学(B),1:87-96,1985.
[101]柳崇键,陶诗言.副热带高压北跳与月尖突变[J].中国科学(B),5:474-480,1983.
[102]李杰友,熊学农,刘秀玉.基于EOF迭代的月径流长期预报[J].河海大学学报,29(2):3,2001.
[103]李跃清.相空间EOF方法及其在气候诊断中的应用[J].高原气象,20(1):2,2001.
[104]田纪伟,孙孚.相空间反演方法及其在海洋资料分析中的应用[J].海洋学报,18(4):1-10,1996.
[105]黄建平,衣育红.利用观测资料反演非线性动力模型[J].中国科学,3(3):331-336,1991.
[106]王凌.智能优化算法及其应用[M].北京:清华大学出版社,2001.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |